Search Results for author: Dinggang Shen

Found 134 papers, 39 papers with code

Two-Phase Multi-Dose-Level PET Image Reconstruction with Dose Level Awareness

no code implementations2 Apr 2024 Yuchen Fei, Yanmei Luo, Yan Wang, Jiaqi Cui, Yuanyuan Xu, Jiliu Zhou, Dinggang Shen

In this paper, to reconstruct high-quality SPET images from multi-dose-level LPET images, we design a novel two-phase multi-dose-level PET reconstruction algorithm with dose level awareness, containing a pre-training phase and a SPET prediction phase.

Image Reconstruction

Revolutionizing Disease Diagnosis with simultaneous functional PET/MR and Deeply Integrated Brain Metabolic, Hemodynamic, and Perfusion Networks

no code implementations29 Mar 2024 Luoyu Wang, Yitian Tao, Qing Yang, Yan Liang, Siwei Liu, Hongcheng Shi, Dinggang Shen, Han Zhang

To fully exploit the inherent complex and nonlinear relation among modalities while producing fine-grained representations for uni-modal inference, we subsequently add a modal alignment module to line up a dominant modality (e. g., PET) with representations of auxiliary modalities (MR).

Human Mesh Recovery from Arbitrary Multi-view Images

no code implementations19 Mar 2024 Xiaoben Li, Mancheng Meng, Ziyan Wu, Terrence Chen, Fan Yang, Dinggang Shen

Human mesh recovery from arbitrary multi-view images involves two characteristics: the arbitrary camera poses and arbitrary number of camera views.

Human Mesh Recovery Pose Estimation

Eye-gaze Guided Multi-modal Alignment Framework for Radiology

1 code implementation19 Mar 2024 Chong Ma, Hanqi Jiang, WenTing Chen, Zihao Wu, Xiaowei Yu, Fang Zeng, Lei Guo, Dajiang Zhu, Tuo Zhang, Dinggang Shen, Tianming Liu, Xiang Li

Additionally, we explore the impact of varying amounts of eye-gaze data on model performance, highlighting the feasibility and utility of integrating this auxiliary data into multi-modal pre-training.

Zero-Shot Learning

Self-learning Canonical Space for Multi-view 3D Human Pose Estimation

no code implementations19 Mar 2024 Xiaoben Li, Mancheng Meng, Ziyan Wu, Terrence Chen, Fan Yang, Dinggang Shen

To facilitate the aggregation of the intra- and inter-view, we define a canonical parameter space, depicted by per-view camera pose and human pose and shape parameters ($\theta$ and $\beta$) of SMPL model, and propose a two-stage learning procedure.

3D Human Pose Estimation Self-Learning

Transferring Ultrahigh-Field Representations for Intensity-Guided Brain Segmentation of Low-Field Magnetic Resonance Imaging

no code implementations13 Feb 2024 Kwanseok Oh, Jieun Lee, Da-Woon Heo, Dinggang Shen, Heung-Il Suk

Specifically, our adaptive fusion module aggregates 7T-like features derived from the LF image by a pre-trained network and then refines them to be effectively assimilable UHF guidance into LF image features.

Brain Image Segmentation Brain Segmentation +3

ScribFormer: Transformer Makes CNN Work Better for Scribble-based Medical Image Segmentation

1 code implementation3 Feb 2024 Zihan Li, Yuan Zheng, Dandan Shan, Shuzhou Yang, Qingde Li, Beizhan Wang, YuanTing Zhang, Qingqi Hong, Dinggang Shen

The proposed ScribFormer model has a triple-branch structure, i. e., the hybrid of a CNN branch, a Transformer branch, and an attention-guided class activation map (ACAM) branch.

Image Segmentation Medical Image Segmentation +2

Image2Points:A 3D Point-based Context Clusters GAN for High-Quality PET Image Reconstruction

1 code implementation1 Feb 2024 Jiaqi Cui, Yan Wang, Lu Wen, Pinxian Zeng, Xi Wu, Jiliu Zhou, Dinggang Shen

To obtain high-quality Positron emission tomography (PET) images while minimizing radiation exposure, numerous methods have been proposed to reconstruct standard-dose PET (SPET) images from the corresponding low-dose PET (LPET) images.

Image Reconstruction

Predicting Infant Brain Connectivity with Federated Multi-Trajectory GNNs using Scarce Data

1 code implementation1 Jan 2024 Michalis Pistos, Gang Li, Weili Lin, Dinggang Shen, Islem Rekik

The three key innovations of FedGmTE-Net++ are: (i) presenting the first federated learning framework specifically designed for brain multi-trajectory evolution prediction in a data-scarce environment, (ii) incorporating an auxiliary regularizer in the local objective function to exploit all the longitudinal brain connectivity within the evolution trajectory and maximize data utilization, (iii) introducing a two-step imputation process, comprising a preliminary KNN-based precompletion followed by an imputation refinement step that employs regressors to improve similarity scores and refine imputations.

Federated Learning Imputation +1

CLIP in Medical Imaging: A Comprehensive Survey

1 code implementation12 Dec 2023 Zihao Zhao, Yuxiao Liu, Han Wu, Yonghao Li, Sheng Wang, Lin Teng, Disheng Liu, Zhiming Cui, Qian Wang, Dinggang Shen

With the aim of facilitating a deeper understanding of this promising direction, this survey offers an in-depth exploration of the CLIP paradigm within the domain of medical imaging, regarding both refined CLIP pre-training and CLIP-driven applications.

Mining Gaze for Contrastive Learning toward Computer-Assisted Diagnosis

1 code implementation11 Dec 2023 Zihao Zhao, Sheng Wang, Qian Wang, Dinggang Shen

Accordingly, we introduce the Medical contrastive Gaze Image Pre-training (McGIP) as a plug-and-play module for contrastive learning frameworks.

Contrastive Learning Semantic Similarity +1

MeLo: Low-rank Adaptation is Better than Fine-tuning for Medical Image Diagnosis

1 code implementation14 Nov 2023 Yitao Zhu, Zhenrong Shen, Zihao Zhao, Sheng Wang, Xin Wang, Xiangyu Zhao, Dinggang Shen, Qian Wang

By fixing the weight of ViT models and only adding small low-rank plug-ins, we achieve competitive results on various diagnosis tasks across different imaging modalities using only a few trainable parameters.

3D Structure-guided Network for Tooth Alignment in 2D Photograph

1 code implementation17 Oct 2023 Yulong Dou, Lanzhuju Mei, Dinggang Shen, Zhiming Cui

In this paper, we propose a 3D structure-guided tooth alignment network that takes 2D photographs as input (e. g., photos captured by smartphones) and aligns the teeth within the 2D image space to generate an orthodontic comparison photograph featuring aesthetically pleasing, aligned teeth.

Contrastive Diffusion Model with Auxiliary Guidance for Coarse-to-Fine PET Reconstruction

1 code implementation20 Aug 2023 Zeyu Han, YuHan Wang, Luping Zhou, Peng Wang, Binyu Yan, Jiliu Zhou, Yan Wang, Dinggang Shen

To obtain high-quality positron emission tomography (PET) scans while reducing radiation exposure to the human body, various approaches have been proposed to reconstruct standard-dose PET (SPET) images from low-dose PET (LPET) images.

Tissue Segmentation of Thick-Slice Fetal Brain MR Scans with Guidance from High-Quality Isotropic Volumes

no code implementations13 Aug 2023 Shijie Huang, Xukun Zhang, Zhiming Cui, He Zhang, Geng Chen, Dinggang Shen

Accurate tissue segmentation of thick-slice fetal brain magnetic resonance (MR) scans is crucial for both reconstruction of isotropic brain MR volumes and the quantification of fetal brain development.

Domain Adaptation Segmentation +1

TriDo-Former: A Triple-Domain Transformer for Direct PET Reconstruction from Low-Dose Sinograms

no code implementations10 Aug 2023 Jiaqi Cui, Pinxian Zeng, Xinyi Zeng, Peng Wang, Xi Wu, Jiliu Zhou, Yan Wang, Dinggang Shen

Specifically, the TriDo-Former consists of two cascaded networks, i. e., a sinogram enhancement transformer (SE-Former) for denoising the input LPET sinograms and a spatial-spectral reconstruction transformer (SSR-Former) for reconstructing SPET images from the denoised sinograms.

Denoising Image Reconstruction +1

Multi-View Vertebra Localization and Identification from CT Images

1 code implementation24 Jul 2023 Han Wu, Jiadong Zhang, Yu Fang, Zhentao Liu, Nizhuan Wang, Zhiming Cui, Dinggang Shen

Additionally, we further propose a Sequence Loss to maintain the sequential structure embedded along the vertebrae.

Contrastive Learning

Accurate 3D Prediction of Missing Teeth in Diverse Patterns for Precise Dental Implant Planning

no code implementations16 Jul 2023 Lei Ma, Peng Xue, Yuning Gu, Yue Zhao, Min Zhu, Zhongxiang Ding, Dinggang Shen

This study presents a novel framework for accurate prediction of missing teeth in different patterns, facilitating digital implant planning.

Review of Large Vision Models and Visual Prompt Engineering

no code implementations3 Jul 2023 Jiaqi Wang, Zhengliang Liu, Lin Zhao, Zihao Wu, Chong Ma, Sigang Yu, Haixing Dai, Qiushi Yang, Yiheng Liu, Songyao Zhang, Enze Shi, Yi Pan, Tuo Zhang, Dajiang Zhu, Xiang Li, Xi Jiang, Bao Ge, Yixuan Yuan, Dinggang Shen, Tianming Liu, Shu Zhang

This review aims to summarize the methods employed in the computer vision domain for large vision models and visual prompt engineering, exploring the latest advancements in visual prompt engineering.

Prompt Engineering

Artificial General Intelligence for Medical Imaging

no code implementations8 Jun 2023 Xiang Li, Lu Zhang, Zihao Wu, Zhengliang Liu, Lin Zhao, Yixuan Yuan, Jun Liu, Gang Li, Dajiang Zhu, Pingkun Yan, Quanzheng Li, Wei Liu, Tianming Liu, Dinggang Shen

In this review, we explore the potential applications of Artificial General Intelligence (AGI) models in healthcare, focusing on foundational Large Language Models (LLMs), Large Vision Models, and Large Multimodal Models.

ChatCAD+: Towards a Universal and Reliable Interactive CAD using LLMs

1 code implementation25 May 2023 Zihao Zhao, Sheng Wang, Jinchen Gu, Yitao Zhu, Lanzhuju Mei, Zixu Zhuang, Zhiming Cui, Qian Wang, Dinggang Shen

However, current works in this field are plagued by limitations, specifically a restricted scope of applicable image domains and the provision of unreliable medical advice.

Retrieval

Learning Better Contrastive View from Radiologist's Gaze

1 code implementation15 May 2023 Sheng Wang, Zixu Zhuang, Xi Ouyang, Lichi Zhang, Zheren Li, Chong Ma, Tianming Liu, Dinggang Shen, Qian Wang

Then, we propose a novel augmentation method, i. e., FocusContrast, to learn from radiologists' gaze in diagnosis and generate contrastive views for medical images with guidance from radiologists' visual attention.

Contrastive Learning Data Augmentation

Mining fMRI Dynamics with Parcellation Prior for Brain Disease Diagnosis

no code implementations4 May 2023 Xiaozhao Liu, Mianxin Liu, Lang Mei, Yuyao Zhang, Feng Shi, Han Zhang, Dinggang Shen

To characterize atypical brain dynamics under diseases, prevalent studies investigate functional magnetic resonance imaging (fMRI).

Graph Learning Multiple Instance Learning

Spatial and Modal Optimal Transport for Fast Cross-Modal MRI Reconstruction

no code implementations4 May 2023 Qi Wang, Zhijie Wen, Jun Shi, Qian Wang, Dinggang Shen, Shihui Ying

Multi-modal magnetic resonance imaging (MRI) plays a crucial role in comprehensive disease diagnosis in clinical medicine.

MRI Reconstruction

Instruction-ViT: Multi-Modal Prompts for Instruction Learning in ViT

no code implementations29 Apr 2023 Zhenxiang Xiao, Yuzhong Chen, Lu Zhang, Junjie Yao, Zihao Wu, Xiaowei Yu, Yi Pan, Lin Zhao, Chong Ma, Xinyu Liu, Wei Liu, Xiang Li, Yixuan Yuan, Dinggang Shen, Dajiang Zhu, Tianming Liu, Xi Jiang

Prompts have been proven to play a crucial role in large language models, and in recent years, vision models have also been using prompts to improve scalability for multiple downstream tasks.

Image Classification

Prompt Engineering for Healthcare: Methodologies and Applications

no code implementations28 Apr 2023 Jiaqi Wang, Enze Shi, Sigang Yu, Zihao Wu, Chong Ma, Haixing Dai, Qiushi Yang, Yanqing Kang, Jinru Wu, Huawen Hu, Chenxi Yue, Haiyang Zhang, Yiheng Liu, Yi Pan, Zhengliang Liu, Lichao Sun, Xiang Li, Bao Ge, Xi Jiang, Dajiang Zhu, Yixuan Yuan, Dinggang Shen, Tianming Liu, Shu Zhang

Prompt engineering is a critical technique in the field of natural language processing that involves designing and optimizing the prompts used to input information into models, aiming to enhance their performance on specific tasks.

Machine Translation Prompt Engineering +3

ChatABL: Abductive Learning via Natural Language Interaction with ChatGPT

no code implementations21 Apr 2023 Tianyang Zhong, Yaonai Wei, Li Yang, Zihao Wu, Zhengliang Liu, Xiaozheng Wei, Wenjun Li, Junjie Yao, Chong Ma, Xiang Li, Dajiang Zhu, Xi Jiang, Junwei Han, Dinggang Shen, Tianming Liu, Tuo Zhang

The proposed method uses the strengths of LLMs' understanding and logical reasoning to correct the incomplete logical facts for optimizing the performance of perceptual module, by summarizing and reorganizing reasoning rules represented in natural language format.

Decipherment Logical Reasoning

Exploring the Trade-Offs: Unified Large Language Models vs Local Fine-Tuned Models for Highly-Specific Radiology NLI Task

no code implementations18 Apr 2023 Zihao Wu, Lu Zhang, Chao Cao, Xiaowei Yu, Haixing Dai, Chong Ma, Zhengliang Liu, Lin Zhao, Gang Li, Wei Liu, Quanzheng Li, Dinggang Shen, Xiang Li, Dajiang Zhu, Tianming Liu

To this end, in this study, we evaluate the performance of ChatGPT/GPT-4 on a radiology NLI task and compare it to other models fine-tuned specifically on task-related data samples.

Specificity Task 2

ImpressionGPT: An Iterative Optimizing Framework for Radiology Report Summarization with ChatGPT

2 code implementations17 Apr 2023 Chong Ma, Zihao Wu, Jiaqi Wang, Shaochen Xu, Yaonai Wei, Zhengliang Liu, Xi Jiang, Lei Guo, Xiaoyan Cai, Shu Zhang, Tuo Zhang, Dajiang Zhu, Dinggang Shen, Tianming Liu, Xiang Li

The 'Impression' section of a radiology report is a critical basis for communication between radiologists and other physicians, and it is typically written by radiologists based on the 'Findings' section.

In-Context Learning

Construction of unbiased dental template and parametric dental model for precision digital dentistry

no code implementations7 Apr 2023 Lei Ma, Jingyang Zhang, Ke Deng, Peng Xue, Zhiming Cui, Yu Fang, Minhui Tang, Yue Zhao, Min Zhu, Zhongxiang Ding, Dinggang Shen

In this study, we develop an unbiased dental template by constructing an accurate dental atlas from CBCT images with guidance of teeth segmentation.

Image Cropping Segmentation

DoctorGLM: Fine-tuning your Chinese Doctor is not a Herculean Task

1 code implementation3 Apr 2023 Honglin Xiong, Sheng Wang, Yitao Zhu, Zihao Zhao, Yuxiao Liu, Linlin Huang, Qian Wang, Dinggang Shen

The recent progress of large language models (LLMs), including ChatGPT and GPT-4, in comprehending and responding to human instructions has been remarkable.

When Brain-inspired AI Meets AGI

no code implementations28 Mar 2023 Lin Zhao, Lu Zhang, Zihao Wu, Yuzhong Chen, Haixing Dai, Xiaowei Yu, Zhengliang Liu, Tuo Zhang, Xintao Hu, Xi Jiang, Xiang Li, Dajiang Zhu, Dinggang Shen, Tianming Liu

Artificial General Intelligence (AGI) has been a long-standing goal of humanity, with the aim of creating machines capable of performing any intellectual task that humans can do.

In-Context Learning

Geometry-Aware Attenuation Field Learning for Sparse-View CBCT Reconstruction

no code implementations26 Mar 2023 Zhentao Liu, Yu Fang, Changjian Li, Han Wu, YuAn Liu, Zhiming Cui, Dinggang Shen

This paper proposes a novel attenuation field encoder-decoder framework by first encoding the volumetric feature from multi-view X-ray projections, then decoding it into the desired attenuation field.

DeID-GPT: Zero-shot Medical Text De-Identification by GPT-4

1 code implementation20 Mar 2023 Zhengliang Liu, Yue Huang, Xiaowei Yu, Lu Zhang, Zihao Wu, Chao Cao, Haixing Dai, Lin Zhao, Yiwei Li, Peng Shu, Fang Zeng, Lichao Sun, Wei Liu, Dinggang Shen, Quanzheng Li, Tianming Liu, Dajiang Zhu, Xiang Li

The digitization of healthcare has facilitated the sharing and re-using of medical data but has also raised concerns about confidentiality and privacy.

Benchmarking De-identification +4

ChatCAD: Interactive Computer-Aided Diagnosis on Medical Image using Large Language Models

1 code implementation14 Feb 2023 Sheng Wang, Zihao Zhao, Xi Ouyang, Qian Wang, Dinggang Shen

Large language models (LLMs) have recently demonstrated their potential in clinical applications, providing valuable medical knowledge and advice.

Decision Making Lesion Segmentation +1

NeuroExplainer: Fine-Grained Attention Decoding to Uncover Cortical Development Patterns of Preterm Infants

no code implementations1 Jan 2023 Chenyu Xue, Fan Wang, Yuanzhuo Zhu, Hui Li, Deyu Meng, Dinggang Shen, Chunfeng Lian

Deploying reliable deep learning techniques in interdisciplinary applications needs learned models to output accurate and (even more importantly) explainable predictions.

Multi-scale Transformer Network with Edge-aware Pre-training for Cross-Modality MR Image Synthesis

2 code implementations2 Dec 2022 Yonghao Li, Tao Zhou, Kelei He, Yi Zhou, Dinggang Shen

To take advantage of both paired and unpaired data, in this paper, we propose a Multi-scale Transformer Network (MT-Net) with edge-aware pre-training for cross-modality MR image synthesis.

Image Generation Image Imputation +3

SNAF: Sparse-view CBCT Reconstruction with Neural Attenuation Fields

no code implementations30 Nov 2022 Yu Fang, Lanzhuju Mei, Changjian Li, YuAn Liu, Wenping Wang, Zhiming Cui, Dinggang Shen

Cone beam computed tomography (CBCT) has been widely used in clinical practice, especially in dental clinics, while the radiation dose of X-rays when capturing has been a long concern in CBCT imaging.

JoJoNet: Joint-contrast and Joint-sampling-and-reconstruction Network for Multi-contrast MRI

no code implementations22 Oct 2022 Lin Zhao, Xiao Chen, Eric Z. Chen, Yikang Liu, Dinggang Shen, Terrence Chen, Shanhui Sun

The proposed framework consists of a sampling mask generator for each image contrast and a reconstructor exploiting the inter-contrast correlations with a recurrent structure which enables the information sharing in a holistic way.

Forecasting Human Trajectory from Scene History

1 code implementation17 Oct 2022 Mancheng Meng, Ziyan Wu, Terrence Chen, Xiran Cai, Xiang Sean Zhou, Fan Yang, Dinggang Shen

We categorize scene history information into two types: historical group trajectory and individual-surroundings interaction.

Trajectory Prediction

Image Synthesis with Disentangled Attributes for Chest X-Ray Nodule Augmentation and Detection

no code implementations19 Jul 2022 Zhenrong Shen, Xi Ouyang, Bin Xiao, Jie-Zhi Cheng, Qian Wang, Dinggang Shen

Moreover, we propose to synthesize nodule CXR images by controlling the disentangled nodule attributes for data augmentation, in order to better compensate for the nodules that are easily missed in the detection task.

Attribute Data Augmentation +2

A Novel Unified Conditional Score-based Generative Framework for Multi-modal Medical Image Completion

no code implementations7 Jul 2022 Xiangxi Meng, Yuning Gu, Yongsheng Pan, Nizhuan Wang, Peng Xue, Mengkang Lu, Xuming He, Yiqiang Zhan, Dinggang Shen

Multi-modal medical image completion has been extensively applied to alleviate the missing modality issue in a wealth of multi-modal diagnostic tasks.

RandStainNA: Learning Stain-Agnostic Features from Histology Slides by Bridging Stain Augmentation and Normalization

1 code implementation25 Jun 2022 Yiqing Shen, Yulin Luo, Dinggang Shen, Jing Ke

To address the problems, we unify SN and SA with a novel RandStainNA scheme, which constrains variable stain styles in a practicable range to train a stain agnostic deep learning model.

Rectify ViT Shortcut Learning by Visual Saliency

no code implementations17 Jun 2022 Chong Ma, Lin Zhao, Yuzhong Chen, David Weizhong Liu, Xi Jiang, Tuo Zhang, Xintao Hu, Dinggang Shen, Dajiang Zhu, Tianming Liu

In this work, we propose a novel and effective saliency-guided vision transformer (SGT) model to rectify shortcut learning in ViT with the absence of eye-gaze data.

Learning towards Synchronous Network Memorizability and Generalizability for Continual Segmentation across Multiple Sites

no code implementations14 Jun 2022 Jingyang Zhang, Peng Xue, Ran Gu, Yuning Gu, Mianxin Liu, Yongsheng Pan, Zhiming Cui, Jiawei Huang, Lei Ma, Dinggang Shen

In clinical practice, a segmentation network is often required to continually learn on a sequential data stream from multiple sites rather than a consolidated set, due to the storage cost and privacy restriction.

Continual Learning

Eye-gaze-guided Vision Transformer for Rectifying Shortcut Learning

no code implementations25 May 2022 Chong Ma, Lin Zhao, Yuzhong Chen, Lu Zhang, Zhenxiang Xiao, Haixing Dai, David Liu, Zihao Wu, Zhengliang Liu, Sheng Wang, Jiaxing Gao, Changhe Li, Xi Jiang, Tuo Zhang, Qian Wang, Dinggang Shen, Dajiang Zhu, Tianming Liu

To address this problem, we propose to infuse human experts' intelligence and domain knowledge into the training of deep neural networks.

Semi-Cycled Generative Adversarial Networks for Real-World Face Super-Resolution

1 code implementation8 May 2022 Hao Hou, Jun Xu, Yingkun Hou, Xiaotao Hu, Benzheng Wei, Dinggang Shen

To better exploit the powerful generative capability of GAN for real-world face SR, in this paper, we establish two independent degradation branches in the forward and backward cycle-consistent reconstruction processes, respectively, while the two processes share the same restoration branch.

Image Restoration Super-Resolution

Two-Stream Graph Convolutional Network for Intra-oral Scanner Image Segmentation

1 code implementation19 Apr 2022 Yue Zhao, Lingming Zhang, Yang Liu, Deyu Meng, Zhiming Cui, Chenqiang Gao, Xinbo Gao, Chunfeng Lian, Dinggang Shen

The state-of-the-art deep learning-based methods often simply concatenate the raw geometric attributes (i. e., coordinates and normal vectors) of mesh cells to train a single-stream network for automatic intra-oral scanner image segmentation.

Graph Learning Image Segmentation +3

Follow My Eye: Using Gaze to Supervise Computer-Aided Diagnosis

1 code implementation6 Apr 2022 Sheng Wang, Xi Ouyang, Tianming Liu, Qian Wang, Dinggang Shen

In this paper, we demonstrate that the eye movement of radiologists reading medical images can be a new form of supervision to train the DNN-based computer-aided diagnosis (CAD) system.

A Long Short-term Memory Based Recurrent Neural Network for Interventional MRI Reconstruction

no code implementations28 Mar 2022 Ruiyang Zhao, Zhao He, Tao Wang, Suhao Qiu, Pawel Herman, Yanle Hu, Chencheng Zhang, Dinggang Shen, Bomin Sun, Guang-Zhong Yang, Yuan Feng

Here we proposed a convolutional long short-term memory (Conv-LSTM) based recurrent neural network (RNN), or ConvLR, to reconstruct interventional images with golden-angle radial sampling.

MRI Reconstruction

Transformers in Medical Image Analysis: A Review

no code implementations24 Feb 2022 Kelei He, Chen Gan, Zhuoyuan Li, Islem Rekik, Zihao Yin, Wen Ji, Yang Gao, Qian Wang, Junfeng Zhang, Dinggang Shen

Transformers have dominated the field of natural language processing, and recently impacted the computer vision area.

Image Generation

Domain Generalization for Mammography Detection via Multi-style and Multi-view Contrastive Learning

1 code implementation21 Nov 2021 Zheren Li, Zhiming Cui, Sheng Wang, Yuji Qi, Xi Ouyang, Qitian Chen, Yuezhi Yang, Zhong Xue, Dinggang Shen, Jie-Zhi Cheng

Specifically, the backbone network is firstly trained with a multi-style and multi-view unsupervised self-learning scheme for the embedding of invariant features to various vendor-styles.

Contrastive Learning Domain Generalization +2

Fast T2w/FLAIR MRI Acquisition by Optimal Sampling of Information Complementary to Pre-acquired T1w MRI

no code implementations11 Nov 2021 Junwei Yang, Xiao-Xin Li, Feihong Liu, Dong Nie, Pietro Lio, Haikun Qi, Dinggang Shen

Recent studies on T1-assisted MRI reconstruction for under-sampled images of other modalities have demonstrated the potential of further accelerating MRI acquisition of other modalities.

MRI Reconstruction

Learning MRI Artifact Removal With Unpaired Data

no code implementations9 Oct 2021 Siyuan Liu, Kim-Han Thung, Liangqiong Qu, Weili Lin, Dinggang Shen, Pew-Thian Yap

Retrospective artifact correction (RAC) improves image quality post acquisition and enhances image usability.

A Few-shot Learning Graph Multi-Trajectory Evolution Network for Forecasting Multimodal Baby Connectivity Development from a Baseline Timepoint

1 code implementation6 Oct 2021 Alaa Bessadok, Ahmed Nebli, Mohamed Ali Mahjoub, Gang Li, Weili Lin, Dinggang Shen, Islem Rekik

To the best of our knowledge, this is the first teacher-student architecture tailored for brain graph multi-trajectory growth prediction that is based on few-shot learning and generalized to graph neural networks (GNNs).

Few-Shot Learning Trajectory Prediction

Two-Stage Mesh Deep Learning for Automated Tooth Segmentation and Landmark Localization on 3D Intraoral Scans

no code implementations24 Sep 2021 Tai-Hsien Wu, Chunfeng Lian, Sanghee Lee, Matthew Pastewait, Christian Piers, Jie Liu, Fang Wang, Li Wang, Chiung-Ying Chiu, Wenchi Wang, Christina Jackson, Wei-Lun Chao, Dinggang Shen, Ching-Chang Ko

Our TS-MDL first adopts an end-to-end \emph{i}MeshSegNet method (i. e., a variant of the existing MeshSegNet with both improved accuracy and efficiency) to label each tooth on the downsampled scan.

Code Generation

Cross-Site Severity Assessment of COVID-19 from CT Images via Domain Adaptation

no code implementations8 Sep 2021 Geng-Xin Xu, Chen Liu, Jun Liu, Zhongxiang Ding, Feng Shi, Man Guo, Wei Zhao, Xiaoming Li, Ying WEI, Yaozong Gao, Chuan-Xian Ren, Dinggang Shen

Particularly, we propose a domain translator and align the heterogeneous data to the estimated class prototypes (i. e., class centers) in a hyper-sphere manifold.

Computed Tomography (CT) Domain Adaptation +1

Multi-Modal MRI Reconstruction Assisted with Spatial Alignment Network

1 code implementation12 Aug 2021 Kai Xuan, Lei Xiang, Xiaoqian Huang, Lichi Zhang, Shu Liao, Dinggang Shen, Qian Wang

However, we find that the performance of the aforementioned multi-modal reconstruction can be negatively affected by subtle spatial misalignment between different modalities, which is actually common in clinical practice.

MRI Reconstruction

Two-Stage Self-Supervised Cycle-Consistency Network for Reconstruction of Thin-Slice MR Images

no code implementations29 Jun 2021 Zhiyang Lu, Zheng Li, Jun Wang, Jun Shi, Dinggang Shen

To this end, we propose a novel Two-stage Self-supervised Cycle-consistency Network (TSCNet) for MR slice interpolation, in which a two-stage self-supervised learning (SSL) strategy is developed for unsupervised DL network training.

Self-Supervised Learning

TSGCNet: Discriminative Geometric Feature Learning With Two-Stream Graph Convolutional Network for 3D Dental Model Segmentation

no code implementations CVPR 2021 Lingming Zhang, Yue Zhao, Deyu Meng, Zhiming Cui, Chenqiang Gao, Xinbo Gao, Chunfeng Lian, Dinggang Shen

State-of-the-art methods directly concatenate the raw attributes of 3D inputs, namely coordinates and normal vectors of mesh cells, to train a single-stream network for fully-automated tooth segmentation.

Graph Learning

Cross-Modality Brain Tumor Segmentation via Bidirectional Global-to-Local Unsupervised Domain Adaptation

1 code implementation17 May 2021 Kelei He, Wen Ji, Tao Zhou, Zhuoyuan Li, Jing Huo, Xin Zhang, Yang Gao, Dinggang Shen, Bing Zhang, Junfeng Zhang

Specifically, a bidirectional image synthesis and segmentation module is proposed to segment the brain tumor using the intermediate data distributions generated for the two domains, which includes an image-to-image translator and a shared-weighted segmentation network.

Brain Tumor Segmentation Image Generation +3

TSGCNet: Discriminative Geometric Feature Learning with Two-Stream GraphConvolutional Network for 3D Dental Model Segmentation

no code implementations26 Dec 2020 Lingming Zhang, Yue Zhao, Deyu Meng, Zhiming Cui, Chenqiang Gao, Xinbo Gao, Chunfeng Lian, Dinggang Shen

State-of-the-art methods directly concatenate the raw attributes of 3D inputs, namely coordinates and normal vectors of mesh cells, to train a single-stream network for fully-automated tooth segmentation.

Graph Learning

Co-evolution of Functional Brain Network at Multiple Scales during Early Infancy

no code implementations15 Sep 2020 Xuyun Wen, Liming Hsu, Weili Lin, Han Zhang, Dinggang Shen

By applying our proposed methodological framework on the collected longitudinal infant dataset, we provided the first evidence that, in the first 2 years of life, the brain functional network is co-evolved at different scales, where each scale displays the unique reconfiguration pattern in terms of modular organization.

Deep Modeling of Growth Trajectories for Longitudinal Prediction of Missing Infant Cortical Surfaces

1 code implementation6 Sep 2020 Peirong Liu, Zhengwang Wu, Gang Li, Pew-Thian Yap, Dinggang Shen

Charting cortical growth trajectories is of paramount importance for understanding brain development.

Learning-based Computer-aided Prescription Model for Parkinson's Disease: A Data-driven Perspective

no code implementations31 Jul 2020 Yinghuan Shi, Wanqi Yang, Kim-Han Thung, Hao Wang, Yang Gao, Yang Pan, Li Zhang, Dinggang Shen

Then, we build a novel computer-aided prescription model by learning the relation between observed symptoms and prescription drug.

Multi-Site Infant Brain Segmentation Algorithms: The iSeg-2019 Challenge

no code implementations4 Jul 2020 Yue Sun, Kun Gao, Zhengwang Wu, Zhihao Lei, Ying WEI, Jun Ma, Xiaoping Yang, Xue Feng, Li Zhao, Trung Le Phan, Jitae Shin, Tao Zhong, Yu Zhang, Lequan Yu, Caizi Li, Ramesh Basnet, M. Omair Ahmad, M. N. S. Swamy, Wenao Ma, Qi Dou, Toan Duc Bui, Camilo Bermudez Noguera, Bennett Landman, Ian H. Gotlib, Kathryn L. Humphreys, Sarah Shultz, Longchuan Li, Sijie Niu, Weili Lin, Valerie Jewells, Gang Li, Dinggang Shen, Li Wang

Deep learning-based methods have achieved state-of-the-art performance; however, one of major limitations is that the learning-based methods may suffer from the multi-site issue, that is, the models trained on a dataset from one site may not be applicable to the datasets acquired from other sites with different imaging protocols/scanners.

Brain Segmentation

HF-UNet: Learning Hierarchically Inter-Task Relevance in Multi-Task U-Net for Accurate Prostate Segmentation

no code implementations21 May 2020 Kelei He, Chunfeng Lian, Bing Zhang, Xin Zhang, Xiaohuan Cao, Dong Nie, Yang Gao, Junfeng Zhang, Dinggang Shen

In this paper, we tackle the challenging task of prostate segmentation in CT images by a two-stage network with 1) the first stage to fast localize, and 2) the second stage to accurately segment the prostate.

Multi-Task Learning Segmentation

A Self-ensembling Framework for Semi-supervised Knee Cartilage Defects Assessment with Dual-Consistency

1 code implementation19 May 2020 Jiayu Huo, Liping Si, Xi Ouyang, Kai Xuan, Weiwu Yao, Zhong Xue, Qian Wang, Dinggang Shen, Lichi Zhang

With dual-consistency checking of the attention in the lesion classification and localization, the two networks can gradually optimize the attention distribution and improve the performance of each other, whereas the training relies on partially labeled data only and follows the semi-supervised manner.

Classification General Classification +2

MetricUNet: Synergistic Image- and Voxel-Level Learning for Precise CT Prostate Segmentation via Online Sampling

no code implementations15 May 2020 Kelei He, Chunfeng Lian, Ehsan Adeli, Jing Huo, Yang Gao, Bing Zhang, Junfeng Zhang, Dinggang Shen

Therefore, the proposed network has a dual-branch architecture that tackles two tasks: 1) a segmentation sub-network aiming to generate the prostate segmentation, and 2) a voxel-metric learning sub-network aiming to improve the quality of the learned feature space supervised by a metric loss.

Metric Learning Multi-Task Learning +2

Synergistic Learning of Lung Lobe Segmentation and Hierarchical Multi-Instance Classification for Automated Severity Assessment of COVID-19 in CT Images

no code implementations8 May 2020 Kelei He, Wei Zhao, Xingzhi Xie, Wen Ji, Mingxia Liu, Zhenyu Tang, Feng Shi, Yang Gao, Jun Liu, Junfeng Zhang, Dinggang Shen

Considering that only a few infection regions in a CT image are related to the severity assessment, we first represent each input image by a bag that contains a set of 2D image patches (with each cropped from a specific slice).

Segmentation

Hypergraph Learning for Identification of COVID-19 with CT Imaging

no code implementations7 May 2020 Donglin Di, Feng Shi, Fuhua Yan, Liming Xia, Zhanhao Mo, Zhongxiang Ding, Fei Shan, Shengrui Li, Ying WEI, Ying Shao, Miaofei Han, Yaozong Gao, He Sui, Yue Gao, Dinggang Shen

The main challenge in early screening is how to model the confusing cases in the COVID-19 and CAP groups, with very similar clinical manifestations and imaging features.

Joint Prediction and Time Estimation of COVID-19 Developing Severe Symptoms using Chest CT Scan

no code implementations7 May 2020 Xiaofeng Zhu, Bin Song, Feng Shi, Yanbo Chen, Rongyao Hu, Jiangzhang Gan, Wenhai Zhang, Man Li, Liye Wang, Yaozong Gao, Fei Shan, Dinggang Shen

To our knowledge, this study is the first work to predict the disease progression and the conversion time, which could help clinicians to deal with the potential severe cases in time or even save the patients' lives.

Classification Computed Tomography (CT) +2

Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19

1 code implementation6 Apr 2020 Feng Shi, Jun Wang, Jun Shi, Ziyan Wu, Qian Wang, Zhenyu Tang, Kelei He, Yinghuan Shi, Dinggang Shen

In this review paper, we thus cover the entire pipeline of medical imaging and analysis techniques involved with COVID-19, including image acquisition, segmentation, diagnosis, and follow-up.

Computed Tomography (CT)

Reducing Magnetic Resonance Image Spacing by Learning Without Ground-Truth

no code implementations27 Mar 2020 Kai Xuan, Liping Si, Lichi Zhang, Zhong Xue, Yining Jiao, Weiwu Yao, Dinggang Shen, Dijia Wu, Qian Wang

In this work, we propose a novel deep-learning-based super-resolution algorithm to generate high-resolution (HR) MR images with small slice spacing from low-resolution (LR) inputs of large slice spacing.

Super-Resolution

Severity Assessment of Coronavirus Disease 2019 (COVID-19) Using Quantitative Features from Chest CT Images

no code implementations26 Mar 2020 Zhenyu Tang, Wei Zhao, Xingzhi Xie, Zheng Zhong, Feng Shi, Jun Liu, Dinggang Shen

Purpose: Using machine learning method to realize automatic severity assessment (non-severe or severe) of COVID-19 based on chest CT images, and to explore the severity-related features from the resulting assessment model.

Computed Tomography (CT)

Lung Infection Quantification of COVID-19 in CT Images with Deep Learning

1 code implementation10 Mar 2020 Fei Shan, Yaozong Gao, Jun Wang, Weiya Shi, Nannan Shi, Miaofei Han, Zhong Xue, Dinggang Shen, Yuxin Shi

The performance of the system was evaluated by comparing the automatically segmented infection regions with the manually-delineated ones on 300 chest CT scans of 300 COVID-19 patients.

COVID-19 Image Segmentation Segmentation

Multifold Acceleration of Diffusion MRI via Slice-Interleaved Diffusion Encoding (SIDE)

no code implementations25 Feb 2020 Yoonmi Hong, Wei-Tang Chang, Geng Chen, Ye Wu, Weili Lin, Dinggang Shen, Pew-Thian Yap

Diffusion MRI (dMRI) is a unique imaging technique for in vivo characterization of tissue microstructure and white matter pathways.

Automatic Data Augmentation via Deep Reinforcement Learning for Effective Kidney Tumor Segmentation

no code implementations22 Feb 2020 Tiexin Qin, Ziyuan Wang, Kelei He, Yinghuan Shi, Yang Gao, Dinggang Shen

Conventional data augmentation realized by performing simple pre-processing operations (\eg, rotation, crop, \etc) has been validated for its advantage in enhancing the performance for medical image segmentation.

Data Augmentation Image Segmentation +5

Automatic Data Augmentation by Learning the Deterministic Policy

1 code implementation18 Oct 2019 Yinghuan Shi, Tiexin Qin, Yong liu, Jiwen Lu, Yang Gao, Dinggang Shen

By introducing an unified optimization goal, DeepAugNet intends to combine the data augmentation and the deep model training in an end-to-end training manner which is realized by simultaneously training a hybrid architecture of dueling deep Q-learning algorithm and a surrogate deep model.

Data Augmentation Q-Learning

Multi-Kernel Filtering for Nonstationary Noise: An Extension of Bilateral Filtering Using Image Context

no code implementations17 Aug 2019 Feihong Liu, Jun Feng, Pew-Thian Yap, Dinggang Shen

Next, a leaf cluster is used to generate one of the multiple kernels, and two corresponding predecessor clusters are used to fine-tune the adopted kernel.

Clustering Denoising +1

Synthesis and Inpainting-Based MR-CT Registration for Image-Guided Thermal Ablation of Liver Tumors

no code implementations30 Jul 2019 Dongming Wei, Sahar Ahmad, Jiayu Huo, Wen Peng, Yunhao Ge, Zhong Xue, Pew-Thian Yap, Wentao Li, Dinggang Shen, Qian Wang

Then, an unsupervised registration network is used to efficiently align the pre-procedural CT (pCT) with the inpainted iCT (inpCT) image.

Image Registration

Dual Adversarial Learning with Attention Mechanism for Fine-grained Medical Image Synthesis

no code implementations7 Jul 2019 Dong Nie, Lei Xiang, Qian Wang, Dinggang Shen

To address this issue, we propose a simple but effective strategy, that is, we propose a dual-discriminator (dual-D) adversarial learning system, in which, a global-D is used to make an overall evaluation for the synthetic image, and a local-D is proposed to densely evaluate the local regions of the synthetic image.

Image Generation

Brain Network Construction and Classification Toolbox (BrainNetClass)

1 code implementation17 Jun 2019 Zhen Zhou, Xiaobo Chen, Yu Zhang, Lishan Qiao, Renping Yu, Gang Pan, Han Zhang, Dinggang Shen

The goal of this work is to introduce a toolbox namely "Brain Network Construction and Classification" (BrainNetClass) to the field to promote more advanced brain network construction methods.

Classification General Classification

Semantic-guided Encoder Feature Learning for Blurry Boundary Delineation

no code implementations10 Jun 2019 Dong Nie, Dinggang Shen

Then we propose a semantic-guided encoder feature learning strategy to learn both high resolution and rich semantic encoder features so that we can more accurately locate the blurry boundaries, which can also enhance the network by selectively learning discriminative features.

Boundary Detection Image Segmentation +3

DeepBundle: Fiber Bundle Parcellation with Graph Convolution Neural Networks

no code implementations7 Jun 2019 Feihong Liu, Jun Feng, Geng Chen, Ye Wu, Yoonmi Hong, Pew-Thian Yap, Dinggang Shen

GCNNs are capable of extracting the geometric features of each fiber tract and harnessing the resulting features for accurate fiber parcellation and ultimately avoiding the use of atlases and any registration method.

Task Decomposition and Synchronization for Semantic Biomedical Image Segmentation

no code implementations21 May 2019 Xuhua Ren, Lichi Zhang, Sahar Ahmad, Dong Nie, Fan Yang, Lei Xiang, Qian Wang, Dinggang Shen

In this paper, we propose to decompose the single segmentation task into three subsequent sub-tasks, including (1) pixel-wise image segmentation, (2) prediction of the class labels of the objects within the image, and (3) classification of the scene the image belonging to.

Brain Tumor Segmentation Image Segmentation +3

Spherical U-Net on Cortical Surfaces: Methods and Applications

no code implementations1 Apr 2019 Fenqiang Zhao, Shunren Xia, Zhengwang Wu, Dingna Duan, Li Wang, Weili Lin, John H Gilmore, Dinggang Shen, Gang Li

In this paper, by leveraging the regular and consistent geometric structure of the resampled cortical surface mapped onto the spherical space, we propose a novel convolution filter analogous to the standard convolution on the image grid.

Attribute Computational Efficiency

Population-Guided Large Margin Classifier for High-Dimension Low -Sample-Size Problems

no code implementations5 Jan 2019 Qingbo Yin, Ehsan Adeli, Liran Shen, Dinggang Shen

Various applications in different fields, such as gene expression analysis or computer vision, suffer from data sets with high-dimensional low-sample-size (HDLSS), which has posed significant challenges for standard statistical and modern machine learning methods.

Face Recognition General Classification

Non-local U-Net for Biomedical Image Segmentation

3 code implementations10 Dec 2018 Zhengyang Wang, Na Zou, Dinggang Shen, Shuiwang Ji

In this work, we propose the non-local U-Nets, which are equipped with flexible global aggregation blocks, for biomedical image segmentation.

Brain Image Segmentation Image Segmentation +2

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

1 code implementation5 Nov 2018 Spyridon Bakas, Mauricio Reyes, Andras Jakab, Stefan Bauer, Markus Rempfler, Alessandro Crimi, Russell Takeshi Shinohara, Christoph Berger, Sung Min Ha, Martin Rozycki, Marcel Prastawa, Esther Alberts, Jana Lipkova, John Freymann, Justin Kirby, Michel Bilello, Hassan Fathallah-Shaykh, Roland Wiest, Jan Kirschke, Benedikt Wiestler, Rivka Colen, Aikaterini Kotrotsou, Pamela Lamontagne, Daniel Marcus, Mikhail Milchenko, Arash Nazeri, Marc-Andre Weber, Abhishek Mahajan, Ujjwal Baid, Elizabeth Gerstner, Dongjin Kwon, Gagan Acharya, Manu Agarwal, Mahbubul Alam, Alberto Albiol, Antonio Albiol, Francisco J. Albiol, Varghese Alex, Nigel Allinson, Pedro H. A. Amorim, Abhijit Amrutkar, Ganesh Anand, Simon Andermatt, Tal Arbel, Pablo Arbelaez, Aaron Avery, Muneeza Azmat, Pranjal B., W Bai, Subhashis Banerjee, Bill Barth, Thomas Batchelder, Kayhan Batmanghelich, Enzo Battistella, Andrew Beers, Mikhail Belyaev, Martin Bendszus, Eze Benson, Jose Bernal, Halandur Nagaraja Bharath, George Biros, Sotirios Bisdas, James Brown, Mariano Cabezas, Shilei Cao, Jorge M. Cardoso, Eric N Carver, Adrià Casamitjana, Laura Silvana Castillo, Marcel Catà, Philippe Cattin, Albert Cerigues, Vinicius S. Chagas, Siddhartha Chandra, Yi-Ju Chang, Shiyu Chang, Ken Chang, Joseph Chazalon, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Chen, Kun Cheng, Ahana Roy Choudhury, Roger Chylla, Albert Clérigues, Steven Colleman, Ramiro German Rodriguez Colmeiro, Marc Combalia, Anthony Costa, Xiaomeng Cui, Zhenzhen Dai, Lutao Dai, Laura Alexandra Daza, Eric Deutsch, Changxing Ding, Chao Dong, Shidu Dong, Wojciech Dudzik, Zach Eaton-Rosen, Gary Egan, Guilherme Escudero, Théo Estienne, Richard Everson, Jonathan Fabrizio, Yong Fan, Longwei Fang, Xue Feng, Enzo Ferrante, Lucas Fidon, Martin Fischer, Andrew P. French, Naomi Fridman, Huan Fu, David Fuentes, Yaozong Gao, Evan Gates, David Gering, Amir Gholami, Willi Gierke, Ben Glocker, Mingming Gong, Sandra González-Villá, T. Grosges, Yuanfang Guan, Sheng Guo, Sudeep Gupta, Woo-Sup Han, Il Song Han, Konstantin Harmuth, Huiguang He, Aura Hernández-Sabaté, Evelyn Herrmann, Naveen Himthani, Winston Hsu, Cheyu Hsu, Xiaojun Hu, Xiaobin Hu, Yan Hu, Yifan Hu, Rui Hua, Teng-Yi Huang, Weilin Huang, Sabine Van Huffel, Quan Huo, Vivek HV, Khan M. Iftekharuddin, Fabian Isensee, Mobarakol Islam, Aaron S. Jackson, Sachin R. Jambawalikar, Andrew Jesson, Weijian Jian, Peter Jin, V Jeya Maria Jose, Alain Jungo, B Kainz, Konstantinos Kamnitsas, Po-Yu Kao, Ayush Karnawat, Thomas Kellermeier, Adel Kermi, Kurt Keutzer, Mohamed Tarek Khadir, Mahendra Khened, Philipp Kickingereder, Geena Kim, Nik King, Haley Knapp, Urspeter Knecht, Lisa Kohli, Deren Kong, Xiangmao Kong, Simon Koppers, Avinash Kori, Ganapathy Krishnamurthi, Egor Krivov, Piyush Kumar, Kaisar Kushibar, Dmitrii Lachinov, Tryphon Lambrou, Joon Lee, Chengen Lee, Yuehchou Lee, M Lee, Szidonia Lefkovits, Laszlo Lefkovits, James Levitt, Tengfei Li, Hongwei Li, Hongyang Li, Xiaochuan Li, Yuexiang Li, Heng Li, Zhenye Li, Xiaoyu Li, Zeju Li, Xiaogang Li, Wenqi Li, Zheng-Shen Lin, Fengming Lin, Pietro Lio, Chang Liu, Boqiang Liu, Xiang Liu, Mingyuan Liu, Ju Liu, Luyan Liu, Xavier Llado, Marc Moreno Lopez, Pablo Ribalta Lorenzo, Zhentai Lu, Lin Luo, Zhigang Luo, Jun Ma, Kai Ma, Thomas Mackie, Anant Madabushi, Issam Mahmoudi, Klaus H. Maier-Hein, Pradipta Maji, CP Mammen, Andreas Mang, B. S. Manjunath, Michal Marcinkiewicz, S McDonagh, Stephen McKenna, Richard McKinley, Miriam Mehl, Sachin Mehta, Raghav Mehta, Raphael Meier, Christoph Meinel, Dorit Merhof, Craig Meyer, Robert Miller, Sushmita Mitra, Aliasgar Moiyadi, David Molina-Garcia, Miguel A. B. Monteiro, Grzegorz Mrukwa, Andriy Myronenko, Jakub Nalepa, Thuyen Ngo, Dong Nie, Holly Ning, Chen Niu, Nicholas K Nuechterlein, Eric Oermann, Arlindo Oliveira, Diego D. C. Oliveira, Arnau Oliver, Alexander F. I. Osman, Yu-Nian Ou, Sebastien Ourselin, Nikos Paragios, Moo Sung Park, Brad Paschke, J. Gregory Pauloski, Kamlesh Pawar, Nick Pawlowski, Linmin Pei, Suting Peng, Silvio M. Pereira, Julian Perez-Beteta, Victor M. Perez-Garcia, Simon Pezold, Bao Pham, Ashish Phophalia, Gemma Piella, G. N. Pillai, Marie Piraud, Maxim Pisov, Anmol Popli, Michael P. Pound, Reza Pourreza, Prateek Prasanna, Vesna Prkovska, Tony P. Pridmore, Santi Puch, Élodie Puybareau, Buyue Qian, Xu Qiao, Martin Rajchl, Swapnil Rane, Michael Rebsamen, Hongliang Ren, Xuhua Ren, Karthik Revanuru, Mina Rezaei, Oliver Rippel, Luis Carlos Rivera, Charlotte Robert, Bruce Rosen, Daniel Rueckert, Mohammed Safwan, Mostafa Salem, Joaquim Salvi, Irina Sanchez, Irina Sánchez, Heitor M. Santos, Emmett Sartor, Dawid Schellingerhout, Klaudius Scheufele, Matthew R. Scott, Artur A. Scussel, Sara Sedlar, Juan Pablo Serrano-Rubio, N. Jon Shah, Nameetha Shah, Mazhar Shaikh, B. Uma Shankar, Zeina Shboul, Haipeng Shen, Dinggang Shen, Linlin Shen, Haocheng Shen, Varun Shenoy, Feng Shi, Hyung Eun Shin, Hai Shu, Diana Sima, M Sinclair, Orjan Smedby, James M. Snyder, Mohammadreza Soltaninejad, Guidong Song, Mehul Soni, Jean Stawiaski, Shashank Subramanian, Li Sun, Roger Sun, Jiawei Sun, Kay Sun, Yu Sun, Guoxia Sun, Shuang Sun, Yannick R Suter, Laszlo Szilagyi, Sanjay Talbar, DaCheng Tao, Zhongzhao Teng, Siddhesh Thakur, Meenakshi H Thakur, Sameer Tharakan, Pallavi Tiwari, Guillaume Tochon, Tuan Tran, Yuhsiang M. Tsai, Kuan-Lun Tseng, Tran Anh Tuan, Vadim Turlapov, Nicholas Tustison, Maria Vakalopoulou, Sergi Valverde, Rami Vanguri, Evgeny Vasiliev, Jonathan Ventura, Luis Vera, Tom Vercauteren, C. A. Verrastro, Lasitha Vidyaratne, Veronica Vilaplana, Ajeet Vivekanandan, Qian Wang, Chiatse J. Wang, Wei-Chung Wang, Duo Wang, Ruixuan Wang, Yuanyuan Wang, Chunliang Wang, Guotai Wang, Ning Wen, Xin Wen, Leon Weninger, Wolfgang Wick, Shaocheng Wu, Qiang Wu, Yihong Wu, Yong Xia, Yanwu Xu, Xiaowen Xu, Peiyuan Xu, Tsai-Ling Yang, Xiaoping Yang, Hao-Yu Yang, Junlin Yang, Haojin Yang, Guang Yang, Hongdou Yao, Xujiong Ye, Changchang Yin, Brett Young-Moxon, Jinhua Yu, Xiangyu Yue, Songtao Zhang, Angela Zhang, Kun Zhang, Xue-jie Zhang, Lichi Zhang, Xiaoyue Zhang, Yazhuo Zhang, Lei Zhang, Jian-Guo Zhang, Xiang Zhang, Tianhao Zhang, Sicheng Zhao, Yu Zhao, Xiaomei Zhao, Liang Zhao, Yefeng Zheng, Liming Zhong, Chenhong Zhou, Xiaobing Zhou, Fan Zhou, Hongtu Zhu, Jin Zhu, Ying Zhuge, Weiwei Zong, Jayashree Kalpathy-Cramer, Keyvan Farahani, Christos Davatzikos, Koen van Leemput, Bjoern Menze

This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.

Brain Tumor Segmentation Survival Prediction +1

Contour Knowledge Transfer for Salient Object Detection

1 code implementation ECCV 2018 Xin Li, Fan Yang, Hong Cheng, Wei Liu, Dinggang Shen

Our goal is to overcome this limitation by automatically converting an existing deep contour detection model into a salient object detection model without using any manual salient object masks.

Contour Detection Object +4

Deep Chronnectome Learning via Full Bidirectional Long Short-Term Memory Networks for MCI Diagnosis

no code implementations30 Aug 2018 Weizheng Yan, Han Zhang, Jing Sui, Dinggang Shen

Dynamic functional connectivity (dFC), consisting of time-varying spatiotemporal dynamics, may characterize "chronnectome" diagnostic information for improving MCI classification.

Clustering General Classification +1

Deep Learning based Inter-Modality Image Registration Supervised by Intra-Modality Similarity

no code implementations28 Apr 2018 Xiaohuan Cao, Jianhua Yang, Li Wang, Zhong Xue, Qian Wang, Dinggang Shen

In this paper, we propose to train a non-rigid inter-modality image registration network, which can directly predict the transformation field from the input multimodal images, such as CT and MR images.

Image Registration

BIRNet: Brain Image Registration Using Dual-Supervised Fully Convolutional Networks

no code implementations13 Feb 2018 Jingfan Fan, Xiaohuan Cao, Pew-Thian Yap, Dinggang Shen

In this paper, we propose a deep learning approach for image registration by predicting deformation from image appearance.

Image Registration

Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation

1 code implementation14 Dec 2017 Jose Dolz, Christian Desrosiers, Li Wang, Jing Yuan, Dinggang Shen, Ismail Ben Ayed

We report evaluations of our method on the public data of the MICCAI iSEG-2017 Challenge on 6-month infant brain MRI segmentation, and show very competitive results among 21 teams, ranking first or second in most metrics.

Image Segmentation Infant Brain Mri Segmentation +3

Deep Embedding Convolutional Neural Network for Synthesizing CT Image from T1-Weighted MR Image

no code implementations7 Sep 2017 Lei Xiang, Qian Wang, Xiyao Jin, Dong Nie, Yu Qiao, Dinggang Shen

After repeat-ing this embedding procedure for several times in the network, we can eventually synthesize a final CT image in the end of the DECNN.

Computed Tomography (CT) Image Generation

Medical Image Synthesis with Context-Aware Generative Adversarial Networks

no code implementations16 Dec 2016 Dong Nie, Roger Trullo, Caroline Petitjean, Su Ruan, Dinggang Shen

To better model the nonlinear relationship from MRI to CT and to produce more realistic images, we propose to use the adversarial training strategy and an image gradient difference loss function.

Computed Tomography (CT) Generative Adversarial Network +1

Robust Feature-Sample Linear Discriminant Analysis for Brain Disorders Diagnosis

no code implementations NeurIPS 2015 Ehsan Adeli-Mosabbeb, Kim-Han Thung, Le An, Feng Shi, Dinggang Shen

The proposed method operates under a semi-supervised setting, in which both labeled training and unlabeled testing data are incorporated to form the intrinsic geometry of the sample space.

General Classification

Joint Coupled-Feature Representation and Coupled Boosting for AD Diagnosis

no code implementations CVPR 2014 Yinghuan Shi, Heung-Il Suk, Yang Gao, Dinggang Shen

Therefore, it is natural to hypothesize that the low-level features extracted from neuroimaging data are related to each other in some ways.

Matrix-Similarity Based Loss Function and Feature Selection for Alzheimer's Disease Diagnosis

no code implementations CVPR 2014 Xiaofeng Zhu, Heung-Il Suk, Dinggang Shen

We conducted experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and showed that the newly devised loss function was effective to enhance the performances of both clinical score prediction and disease status identification, outperforming the state-of-the-art methods.

feature selection

Regularized Spherical Polar Fourier Diffusion MRI with Optimal Dictionary Learning

no code implementations2 Jul 2013 Jian Cheng, Tianzi Jiang, Rachid Deriche, Dinggang Shen, Pew-Thian Yap

Compared with the start-of-the-art CR-DL and DR-DL methods proposed by Merlet et al. and Bilgic et al., espectively, our work offers the following advantages.

Dictionary Learning

Discriminative Brain Effective Connectivity Analysis for Alzheimer's Disease: A Kernel Learning Approach upon Sparse Gaussian Bayesian Network

no code implementations CVPR 2013 Luping Zhou, Lei Wang, Lingqiao Liu, Philip Ogunbona, Dinggang Shen

Analyzing brain networks from neuroimages is becoming a promising approach in identifying novel connectivitybased biomarkers for the Alzheimer's disease (AD).

Prostate Segmentation in CT Images via Spatial-Constrained Transductive Lasso

no code implementations CVPR 2013 Yinghuan Shi, Shu Liao, Yaozong Gao, Daoqiang Zhang, Yang Gao, Dinggang Shen

Specifically, to segment the prostate in the current treatment image, the physician first takes a few seconds to manually specify the first and last slices of the prostate in the image space.

Segmentation

Groupwise Registration via Graph Shrinkage on the Image Manifold

no code implementations CVPR 2013 Shihui Ying, Guorong Wu, Qian Wang, Dinggang Shen

Specifically, we first use a graph to model the distribution of all image data sitting on the image manifold, with each node representing an image and each edge representing the geodesic pathway between two nodes (or images).

Image Registration

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