Search Results for author: Shaoting Zhang

Found 86 papers, 51 papers with code

ViLaM: A Vision-Language Model with Enhanced Visual Grounding and Generalization Capability

1 code implementation21 Nov 2023 Xiaoyu Yang, Lijian Xu, Hongsheng Li, Shaoting Zhang

This approach enables us to optimally utilize the knowledge and reasoning capacities of large pre-trained language models for an array of tasks encompassing both language and vision.

Language Modelling Large Language Model +3

Learning A Multi-Task Transformer Via Unified And Customized Instruction Tuning For Chest Radiograph Interpretation

1 code implementation2 Nov 2023 Lijian Xu, Ziyu Ni, Xinglong Liu, Xiaosong Wang, Hongsheng Li, Shaoting Zhang

We first compose a multi-task training dataset comprising 13. 4 million instruction and ground-truth pairs (with approximately one million radiographs) for the customized tuning, involving both image- and pixel-level tasks.


1 code implementation23 Oct 2023 Haoyu Wang, Sizheng Guo, Jin Ye, Zhongying Deng, Junlong Cheng, Tianbin Li, Jianpin Chen, Yanzhou Su, Ziyan Huang, Yiqing Shen, Bin Fu, Shaoting Zhang, Junjun He, Yu Qiao

These issues can hardly be addressed by fine-tuning SAM on medical data because the original 2D structure of SAM neglects 3D spatial information.

Image Segmentation Semantic Segmentation

Boosting Dermatoscopic Lesion Segmentation via Diffusion Models with Visual and Textual Prompts

no code implementations4 Oct 2023 Shiyi Du, Xiaosong Wang, Yongyi Lu, Yuyin Zhou, Shaoting Zhang, Alan Yuille, Kang Li, Zongwei Zhou

Image synthesis approaches, e. g., generative adversarial networks, have been popular as a form of data augmentation in medical image analysis tasks.

Data Augmentation Image Generation +2

UPL-SFDA: Uncertainty-aware Pseudo Label Guided Source-Free Domain Adaptation for Medical Image Segmentation

1 code implementation19 Sep 2023 Jianghao Wu, Guotai Wang, Ran Gu, Tao Lu, Yinan Chen, Wentao Zhu, Tom Vercauteren, Sébastien Ourselin, Shaoting Zhang

The different predictions in these duplicated heads are used to obtain pseudo labels for unlabeled target-domain images and their uncertainty to identify reliable pseudo labels.

Brain Segmentation Image Segmentation +5

Scribble-based 3D Multiple Abdominal Organ Segmentation via Triple-branch Multi-dilated Network with Pixel- and Class-wise Consistency

no code implementations18 Sep 2023 Meng Han, Xiangde Luo, Wenjun Liao, Shichuan Zhang, Shaoting Zhang, Guotai Wang

Specifically, we employ a Triple-branch multi-Dilated network (TDNet) with one encoder and three decoders using different dilation rates to capture features from different receptive fields that are complementary to each other to generate high-quality soft pseudo labels.

Computed Tomography (CT) Organ Segmentation +2

A-Eval: A Benchmark for Cross-Dataset Evaluation of Abdominal Multi-Organ Segmentation

2 code implementations7 Sep 2023 Ziyan Huang, Zhongying Deng, Jin Ye, Haoyu Wang, Yanzhou Su, Tianbin Li, Hui Sun, Junlong Cheng, Jianpin Chen, Junjun He, Yun Gu, Shaoting Zhang, Lixu Gu, Yu Qiao

To address these questions, we introduce A-Eval, a benchmark for the cross-dataset Evaluation ('Eval') of Abdominal ('A') multi-organ segmentation.

Organ Segmentation Segmentation

LLM-Mini-CEX: Automatic Evaluation of Large Language Model for Diagnostic Conversation

no code implementations15 Aug 2023 Xiaoming Shi, Jie Xu, Jinru Ding, Jiali Pang, Sichen Liu, Shuqing Luo, Xingwei Peng, Lu Lu, Haihong Yang, Mingtao Hu, Tong Ruan, Shaoting Zhang

Despite their alluring technological potential, there is no unified and comprehensive evaluation criterion, leading to the inability to evaluate the quality and potential risks of medical LLMs, further hindering the application of LLMs in medical treatment scenarios.

Language Modelling Large Language Model +1

Classification of lung cancer subtypes on CT images with synthetic pathological priors

no code implementations9 Aug 2023 Wentao Zhu, Yuan Jin, Gege Ma, Geng Chen, Jan Egger, Shaoting Zhang, Dimitris N. Metaxas

The accurate diagnosis on pathological subtypes for lung cancer is of significant importance for the follow-up treatments and prognosis managements.

Computed Tomography (CT)

Pathology-and-genomics Multimodal Transformer for Survival Outcome Prediction

1 code implementation22 Jul 2023 Kexin Ding, Mu Zhou, Dimitris N. Metaxas, Shaoting Zhang

Survival outcome assessment is challenging and inherently associated with multiple clinical factors (e. g., imaging and genomics biomarkers) in cancer.

Survival Prediction whole slide images

Foundation Model for Endoscopy Video Analysis via Large-scale Self-supervised Pre-train

1 code implementation29 Jun 2023 Zhao Wang, Chang Liu, Shaoting Zhang, Qi Dou

Foundation models have exhibited remarkable success in various applications, such as disease diagnosis and text report generation.

Segmentation Transfer Learning

MIS-FM: 3D Medical Image Segmentation using Foundation Models Pretrained on a Large-Scale Unannotated Dataset

1 code implementation29 Jun 2023 Guotai Wang, Jianghao Wu, Xiangde Luo, Xinglong Liu, Kang Li, Shaoting Zhang

The proposed model was pretrained with 110k unannotated 3D CT volumes, and experiments with different downstream segmentation targets including head and neck organs, thoracic/abdominal organs showed that our pretrained model largely outperformed training from scratch and several state-of-the-art self-supervised training methods and segmentation models.

Image Segmentation Medical Image Segmentation +3

MedLSAM: Localize and Segment Anything Model for 3D CT Images

1 code implementation26 Jun 2023 Wenhui Lei, Xu Wei, Xiaofan Zhang, Kang Li, Shaoting Zhang

Our findings are twofold: 1) MedLAM is capable of directly localizing any anatomical structure using just a few template scans, yet its performance surpasses that of fully supervised models; 2) MedLSAM not only aligns closely with the performance of SAM and its specialized medical adaptations with manual prompts but achieves this with minimal reliance on extreme point annotations across the entire dataset.

Image Segmentation Semantic Segmentation

KiUT: Knowledge-injected U-Transformer for Radiology Report Generation

no code implementations CVPR 2023 Zhongzhen Huang, Xiaofan Zhang, Shaoting Zhang

Radiology report generation aims to automatically generate a clinically accurate and coherent paragraph from the X-ray image, which could relieve radiologists from the heavy burden of report writing.

Clinical Knowledge

UM-CAM: Uncertainty-weighted Multi-resolution Class Activation Maps for Weakly-supervised Fetal Brain Segmentation

no code implementations20 Jun 2023 Jia Fu, Tao Lu, Shaoting Zhang, Guotai Wang

To this end, we propose a novel weakly-supervised method with image-level labels based on semantic features and context information exploration.

Brain Segmentation Weakly supervised segmentation

On the Challenges and Perspectives of Foundation Models for Medical Image Analysis

no code implementations9 Jun 2023 Shaoting Zhang, Dimitris Metaxas

This article discusses the opportunities, applications and future directions of large-scale pre-trained models, i. e., foundation models, for analyzing medical images.

MidMed: Towards Mixed-Type Dialogues for Medical Consultation

1 code implementation5 Jun 2023 Xiaoming Shi, Zeming Liu, Chuan Wang, Haitao Leng, Kui Xue, Xiaofan Zhang, Shaoting Zhang

To mitigate this challenge, we propose a novel task and create a human-to-human mixed-type medical consultation dialogue corpus, termed MidMed, covering five dialogue types: task-oriented dialogue for diagnosis, recommendation, knowledge-grounded dialogue, QA, and chitchat.

Dialogue Generation

Training Like a Medical Resident: Universal Medical Image Segmentation via Context Prior Learning

2 code implementations4 Jun 2023 Yunhe Gao, Zhuowei Li, Di Liu, Mu Zhou, Shaoting Zhang, Dimitris N. Metaxas

Inspired by the training of medical residents, we explore universal medical image segmentation, whose goal is to learn from diverse medical imaging sources covering a range of clinical targets, body regions, and image modalities.

Image Segmentation Incremental Learning +4

ConES: Concept Embedding Search for Parameter Efficient Tuning Large Vision Language Models

no code implementations30 May 2023 Huahui Yi, Ziyuan Qin, Wei Xu, Miaotian Guo, Kun Wang, Shaoting Zhang, Kang Li, Qicheng Lao

To achieve this, we propose a Concept Embedding Search (ConES) approach by optimizing prompt embeddings -- without the need of the text encoder -- to capture the 'concept' of the image modality through a variety of task objectives.

Instance Segmentation Prompt Engineering +1

Semi-supervised Pathological Image Segmentation via Cross Distillation of Multiple Attentions

1 code implementation30 May 2023 Lanfeng Zhong, Xin Liao, Shaoting Zhang, Guotai Wang

In this paper, we propose a novel SSL method based on Cross Distillation of Multiple Attentions (CDMA) to effectively leverage unlabeled images.

Image Segmentation Knowledge Distillation +1

Voxel2Hemodynamics: An End-to-end Deep Learning Method for Predicting Coronary Artery Hemodynamics

no code implementations30 May 2023 Ziyu Ni, Linda Wei, Lijian Xu, Simon Yu, Qing Xia, Hongsheng Li, Shaoting Zhang

In this work, we proposed an end-to-end deep learning framework, which could predict the coronary artery hemodynamics from CCTA images.

SAM on Medical Images: A Comprehensive Study on Three Prompt Modes

no code implementations28 Apr 2023 Dongjie Cheng, Ziyuan Qin, Zekun Jiang, Shaoting Zhang, Qicheng Lao, Kang Li

As the first promptable foundation model for segmentation tasks, it was trained on a large dataset with an unprecedented number of images and annotations.

Image Segmentation Medical Image Segmentation +1

Towards General Purpose Medical AI: Continual Learning Medical Foundation Model

no code implementations12 Mar 2023 Huahui Yi, Ziyuan Qin, Qicheng Lao, Wei Xu, Zekun Jiang, Dequan Wang, Shaoting Zhang, Kang Li

Therefore, in this work, we further explore the possibility of leveraging pre-trained VLMs as medical foundation models for building general-purpose medical AI, where we thoroughly investigate three machine-learning paradigms, i. e., domain/task-specialized learning, joint learning, and continual learning, for training the VLMs and evaluate their generalization performance on cross-domain and cross-task test sets.

Continual Learning

CDDSA: Contrastive Domain Disentanglement and Style Augmentation for Generalizable Medical Image Segmentation

1 code implementation22 Nov 2022 Ran Gu, Guotai Wang, Jiangshan Lu, Jingyang Zhang, Wenhui Lei, Yinan Chen, Wenjun Liao, Shichuan Zhang, Kang Li, Dimitris N. Metaxas, Shaoting Zhang

First, a disentangle network is proposed to decompose an image into a domain-invariant anatomical representation and a domain-specific style code, where the former is sent to a segmentation model that is not affected by the domain shift, and the disentangle network is regularized by a decoder that combines the anatomical and style codes to reconstruct the input image.

Disentanglement Domain Generalization +4

PyMIC: A deep learning toolkit for annotation-efficient medical image segmentation

1 code implementation19 Aug 2022 Guotai Wang, Xiangde Luo, Ran Gu, Shuojue Yang, Yijie Qu, Shuwei Zhai, Qianfei Zhao, Kang Li, Shaoting Zhang

Existing toolkits mainly focus on fully supervised segmentation and require full and accurate pixel-level annotations that are time-consuming and difficult to acquire for segmentation tasks, which makes learning from imperfect labels highly desired for reducing the annotation cost.

Image Segmentation Medical Image Segmentation +3

Contrastive Semi-supervised Learning for Domain Adaptive Segmentation Across Similar Anatomical Structures

1 code implementation18 Aug 2022 Ran Gu, Jingyang Zhang, Guotai Wang, Wenhui Lei, Tao Song, Xiaofan Zhang, Kang Li, Shaoting Zhang

To solve this problem, we propose Contrastive Semi-supervised learning for Cross Anatomy Domain Adaptation (CS-CADA) that adapts a model to segment similar structures in a target domain, which requires only limited annotations in the target domain by leveraging a set of existing annotated images of similar structures in a source domain.

Anatomy Contrastive Learning +4

PA-Seg: Learning from Point Annotations for 3D Medical Image Segmentation using Contextual Regularization and Cross Knowledge Distillation

1 code implementation11 Aug 2022 Shuwei Zhai, Guotai Wang, Xiangde Luo, Qiang Yue, Kang Li, Shaoting Zhang

The success of Convolutional Neural Networks (CNNs) in 3D medical image segmentation relies on massive fully annotated 3D volumes for training that are time-consuming and labor-intensive to acquire.

Brain Tumor Segmentation Image Segmentation +4

Towards Self-supervised and Weight-preserving Neural Architecture Search

1 code implementation8 Jun 2022 Zhuowei Li, Yibo Gao, Zhenzhou Zha, Zhiqiang Hu, Qing Xia, Shaoting Zhang, Dimitris N. Metaxas

In this work, we propose the self-supervised and weight-preserving neural architecture search (SSWP-NAS) as an extension of the current NAS framework by allowing the self-supervision and retaining the concomitant weights discovered during the search stage.

Neural Architecture Search

HMRNet: High and Multi-Resolution Network with Bidirectional Feature Calibration for Brain Structure Segmentation in Radiotherapy

1 code implementation7 Jun 2022 Hao Fu, Guotai Wang, Wenhui Lei, Wei Xu, Qianfei Zhao, Shichuan Zhang, Kang Li, Shaoting Zhang

Accurate segmentation of Anatomical brain Barriers to Cancer spread (ABCs) plays an important role for automatic delineation of Clinical Target Volume (CTV) of brain tumors in radiotherapy.


Graph Convolutional Networks for Multi-modality Medical Imaging: Methods, Architectures, and Clinical Applications

no code implementations17 Feb 2022 Kexin Ding, Mu Zhou, Zichen Wang, Qiao Liu, Corey W. Arnold, Shaoting Zhang, Dimitri N. Metaxas

Image-based characterization and disease understanding involve integrative analysis of morphological, spatial, and topological information across biological scales.

Contrastive and Selective Hidden Embeddings for Medical Image Segmentation

1 code implementation21 Jan 2022 Zhuowei Li, Zihao Liu, Zhiqiang Hu, Qing Xia, Ruiqin Xiong, Shaoting Zhang, Dimitris Metaxas, Tingting Jiang

Medical image segmentation has been widely recognized as a pivot procedure for clinical diagnosis, analysis, and treatment planning.

Contrastive Learning feature selection +4

Semi-Supervised Medical Image Segmentation via Cross Teaching between CNN and Transformer

1 code implementation9 Dec 2021 Xiangde Luo, Minhao Hu, Tao Song, Guotai Wang, Shaoting Zhang

Notably, this work may be the first attempt to combine CNN and transformer for semi-supervised medical image segmentation and achieve promising results on a public benchmark.

Image Segmentation Pseudo Label +3

One-shot Weakly-Supervised Segmentation in Medical Images

1 code implementation21 Nov 2021 Wenhui Lei, Qi Su, Ran Gu, Na Wang, Xinglong Liu, Guotai Wang, Xiaofan Zhang, Shaoting Zhang

Deep neural networks usually require accurate and a large number of annotations to achieve outstanding performance in medical image segmentation.

Denoising Image Segmentation +5

WORD: A large scale dataset, benchmark and clinical applicable study for abdominal organ segmentation from CT image

3 code implementations3 Nov 2021 Xiangde Luo, Wenjun Liao, Jianghong Xiao, Jieneng Chen, Tao Song, Xiaofan Zhang, Kang Li, Dimitris N. Metaxas, Guotai Wang, Shaoting Zhang

Deep learning-based medical image segmentation has shown the potential to reduce manual delineation efforts, but it still requires a large-scale fine annotated dataset for training, and there is a lack of large-scale datasets covering the whole abdomen region with accurate and detailed annotations for the whole abdominal organ segmentation.

Image Segmentation Medical Image Segmentation +4

Semi-Supervised Segmentation of Radiation-Induced Pulmonary Fibrosis from Lung CT Scans with Multi-Scale Guided Dense Attention

1 code implementation29 Sep 2021 Guotai Wang, Shuwei Zhai, Giovanni Lasio, Baoshe Zhang, Byong Yi, Shifeng Chen, Thomas J. Macvittie, Dimitris Metaxas, Jinghao Zhou, Shaoting Zhang

Computed Tomography (CT) plays an important role in monitoring radiation-induced Pulmonary Fibrosis (PF), where accurate segmentation of the PF lesions is highly desired for diagnosis and treatment follow-up.

Computed Tomography (CT) Lesion Segmentation +1

Domain Composition and Attention for Unseen-Domain Generalizable Medical Image Segmentation

1 code implementation18 Sep 2021 Ran Gu, Jingyang Zhang, Rui Huang, Wenhui Lei, Guotai Wang, Shaoting Zhang

First, we present a domain composition method that represents one certain domain by a linear combination of a set of basis representations (i. e., a representation bank).

Domain Generalization Image Segmentation +2

Multi-frame Collaboration for Effective Endoscopic Video Polyp Detection via Spatial-Temporal Feature Transformation

1 code implementation8 Jul 2021 Lingyun Wu, Zhiqiang Hu, Yuanfeng Ji, Ping Luo, Shaoting Zhang

For example, STFT improves the still image baseline FCOS by 10. 6% and 20. 6% on the comprehensive F1-score of the polyp localization task in CVC-Clinic and ASUMayo datasets, respectively, and outperforms the state-of-the-art video-based method by 3. 6% and 8. 0%, respectively.

Hybrid Supervision Learning for Pathology Whole Slide Image Classification

1 code implementation2 Jul 2021 Jiahui Li, Wen Chen, Xiaodi Huang, Zhiqiang Hu, Qi Duan, Hongsheng Li, Dimitris N. Metaxas, Shaoting Zhang

To handle this problem, we propose a hybrid supervision learning framework for this kind of high resolution images with sufficient image-level coarse annotations and a few pixel-level fine labels.

Classification Image Classification +3

Multi-Compound Transformer for Accurate Biomedical Image Segmentation

1 code implementation28 Jun 2021 Yuanfeng Ji, Ruimao Zhang, Huijie Wang, Zhen Li, Lingyun Wu, Shaoting Zhang, Ping Luo

The recent vision transformer(i. e. for image classification) learns non-local attentive interaction of different patch tokens.

Image Classification Image Segmentation +2

Self-Ensembling Contrastive Learning for Semi-Supervised Medical Image Segmentation

no code implementations27 May 2021 Jinxi Xiang, Zhuowei Li, Wenji Wang, Qing Xia, Shaoting Zhang

In this paper, we aim to boost the performance of semi-supervised learning for medical image segmentation with limited labels using a self-ensembling contrastive learning technique.

Contrastive Learning Image Segmentation +3

Learning Unknown from Correlations: Graph Neural Network for Inter-novel-protein Interaction Prediction

1 code implementation14 May 2021 Guofeng Lv, Zhiqiang Hu, Yanguang Bi, Shaoting Zhang

The study of multi-type Protein-Protein Interaction (PPI) is fundamental for understanding biological processes from a systematic perspective and revealing disease mechanisms.

MIDeepSeg: Minimally Interactive Segmentation of Unseen Objects from Medical Images Using Deep Learning

2 code implementations25 Apr 2021 Xiangde Luo, Guotai Wang, Tao Song, Jingyang Zhang, Michael Aertsen, Jan Deprest, Sebastien Ourselin, Tom Vercauteren, Shaoting Zhang

To solve these problems, we propose a novel deep learning-based interactive segmentation method that not only has high efficiency due to only requiring clicks as user inputs but also generalizes well to a range of previously unseen objects.

Image Segmentation Interactive Segmentation +3

SCPM-Net: An Anchor-free 3D Lung Nodule Detection Network using Sphere Representation and Center Points Matching

1 code implementation12 Apr 2021 Xiangde Luo, Tao Song, Guotai Wang, Jieneng Chen, Yinan Chen, Kang Li, Dimitris N. Metaxas, Shaoting Zhang

To overcome these problems, we propose a 3D sphere representation-based center-points matching detection network that is anchor-free and automatically predicts the position, radius, and offset of nodules without the manual design of nodule/anchor parameters.

Lung Nodule Detection

Automatic Segmentation of Organs-at-Risk from Head-and-Neck CT using Separable Convolutional Neural Network with Hard-Region-Weighted Loss

1 code implementation3 Feb 2021 Wenhui Lei, Haochen Mei, Zhengwentai Sun, Shan Ye, Ran Gu, Huan Wang, Rui Huang, Shichuan Zhang, Shaoting Zhang, Guotai Wang

Despite the stateof-the-art performance achieved by Convolutional Neural Networks (CNNs) for automatic segmentation of OARs, existing methods do not provide uncertainty estimation of the segmentation results for treatment planning, and their accuracy is still limited by several factors, including the low contrast of soft tissues in CT, highly imbalanced sizes of OARs and large inter-slice spacing.

Computed Tomography (CT) Segmentation

Annotation-Efficient Learning for Medical Image Segmentation based on Noisy Pseudo Labels and Adversarial Learning

no code implementations29 Dec 2020 Lu Wang, Dong Guo, Guotai Wang, Shaoting Zhang

In this paper, we propose an annotation-efficient learning framework for segmentation tasks that avoids annotations of training images, where we use an improved Cycle-Consistent Generative Adversarial Network (GAN) to learn from a set of unpaired medical images and auxiliary masks obtained either from a shape model or public datasets.

Image Segmentation Medical Image Segmentation +1

Multi-modal AsynDGAN: Learn From Distributed Medical Image Data without Sharing Private Information

no code implementations15 Dec 2020 Qi Chang, Zhennan Yan, Lohendran Baskaran, Hui Qu, Yikai Zhang, Tong Zhang, Shaoting Zhang, Dimitris N. Metaxas

As deep learning technologies advance, increasingly more data is necessary to generate general and robust models for various tasks.

Efficient Semi-Supervised Gross Target Volume of Nasopharyngeal Carcinoma Segmentation via Uncertainty Rectified Pyramid Consistency

1 code implementation13 Dec 2020 Xiangde Luo, Wenjun Liao, Jieneng Chen, Tao Song, Yinan Chen, Shichuan Zhang, Nianyong Chen, Guotai Wang, Shaoting Zhang

In this paper, we propose a novel framework with Uncertainty Rectified Pyramid Consistency (URPC) regularization for semi-supervised NPC GTV segmentation.


Contrastive Learning of Relative Position Regression for One-Shot Object Localization in 3D Medical Images

2 code implementations13 Dec 2020 Wenhui Lei, Wei Xu, Ran Gu, Hao Fu, Shaoting Zhang, Guotai Wang

To address this problem, we present a one-shot framework for organ and landmark localization in volumetric medical images, which does not need any annotation during the training stage and could be employed to locate any landmarks or organs in test images given a support (reference) image during the inference stage.

Contrastive Learning Object Localization +2

Learning Euler's Elastica Model for Medical Image Segmentation

1 code implementation1 Nov 2020 Xu Chen, Xiangde Luo, Yitian Zhao, Shaoting Zhang, Guotai Wang, Yalin Zheng

Inspired by Euler's Elastica model and recent active contour models introduced into the field of deep learning, we propose a novel active contour with elastica (ACE) loss function incorporating Elastica (curvature and length) and region information as geometrically-natural constraints for the image segmentation tasks.

Image Segmentation Medical Image Segmentation +2

CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation

3 code implementations22 Sep 2020 Ran Gu, Guotai Wang, Tao Song, Rui Huang, Michael Aertsen, Jan Deprest, Sébastien Ourselin, Tom Vercauteren, Shaoting Zhang

Also, we propose a scale attention module implicitly emphasizing the most salient feature maps among multiple scales so that the CNN is adaptive to the size of an object.

Image Segmentation Lesion Segmentation +3

UXNet: Searching Multi-level Feature Aggregation for 3D Medical Image Segmentation

no code implementations16 Sep 2020 Yuanfeng Ji, Ruimao Zhang, Zhen Li, Jiamin Ren, Shaoting Zhang, Ping Luo

Unlike the recent neural architecture search (NAS) methods that typically searched the optimal operators in each network layer, but missed a good strategy to search for feature aggregations, this paper proposes a novel NAS method for 3D medical image segmentation, named UXNet, which searches both the scale-wise feature aggregation strategies as well as the block-wise operators in the encoder-decoder network.

Image Segmentation Neural Architecture Search +3

Semi-supervised Medical Image Segmentation through Dual-task Consistency

1 code implementation9 Sep 2020 Xiangde Luo, Jieneng Chen, Tao Song, Yinan Chen, Guotai Wang, Shaoting Zhang

Concretely, we use a dual-task deep network that jointly predicts a pixel-wise segmentation map and a geometry-aware level set representation of the target.

Image Segmentation Segmentation +2

Multi-organ Segmentation via Co-training Weight-averaged Models from Few-organ Datasets

no code implementations17 Aug 2020 Rui Huang, Yuanjie Zheng, Zhiqiang Hu, Shaoting Zhang, Hongsheng Li

In most scenarios, one might obtain annotations of a single or a few organs from one training set, and obtain annotations of the the other organs from another set of training images.

Organ Segmentation

Weakly Supervised Deep Nuclei Segmentation Using Partial Points Annotation in Histopathology Images

no code implementations10 Jul 2020 Hui Qu, Pengxiang Wu, Qiaoying Huang, Jingru Yi, Zhennan Yan, Kang Li, Gregory M. Riedlinger, Subhajyoti De, Shaoting Zhang, Dimitris N. Metaxas

To alleviate such tedious and manual effort, in this paper we propose a novel weakly supervised segmentation framework based on partial points annotation, i. e., only a small portion of nuclei locations in each image are labeled.

Segmentation Weakly supervised segmentation

Automatic Ischemic Stroke Lesion Segmentation from Computed Tomography Perfusion Images by Image Synthesis and Attention-Based Deep Neural Networks

no code implementations7 Jul 2020 Guotai Wang, Tao Song, Qiang Dong, Mei Cui, Ning Huang, Shaoting Zhang

Experimental results showed that our framework achieved the top performance on ISLES 2018 challenge and: 1) our method using synthesized pseudo DWI outperformed methods segmenting the lesion from perfusion parameter maps directly; 2) the feature extractor exploiting additional spatiotemporal CTA images led to better synthesized pseudo DWI quality and higher segmentation accuracy; and 3) the proposed loss functions and network structure improved the pseudo DWI synthesis and lesion segmentation performance.

Image Generation Ischemic Stroke Lesion Segmentation +2

Uncertainty-Guided Efficient Interactive Refinement of Fetal Brain Segmentation from Stacks of MRI Slices

1 code implementation2 Jul 2020 Guotai Wang, Michael Aertsen, Jan Deprest, Sebastien Ourselin, Tom Vercauteren, Shaoting Zhang

Experimental results show that: (1) our proposed CNN obtains uncertainty estimation in real time which correlates well with mis-segmentations, (2) the proposed interactive level set is effective and efficient for refinement, (3) UGIR obtains accurate refinement results with around 30% improvement of efficiency by using uncertainty to guide user interactions.

Brain Segmentation Segmentation

Weakly Supervised Vessel Segmentation in X-ray Angiograms by Self-Paced Learning from Noisy Labels with Suggestive Annotation

no code implementations27 May 2020 Jingyang Zhang, Guotai Wang, Hongzhi Xie, Shuyang Zhang, Ning Huang, Shaoting Zhang, Lixu Gu

The segmentation of coronary arteries in X-ray angiograms by convolutional neural networks (CNNs) is promising yet limited by the requirement of precisely annotating all pixels in a large number of training images, which is extremely labor-intensive especially for complex coronary trees.

Weakly-supervised Learning

SenseCare: A Research Platform for Medical Image Informatics and Interactive 3D Visualization

no code implementations3 Apr 2020 Qi Duan, Guotai Wang, Rui Wang, Chao Fu, Xinjun Li, Maoliang Gong, Xinglong Liu, Qing Xia, Xiaodi Huang, Zhiqiang Hu, Ning Huang, Shaoting Zhang

To this end, we have developed SenseCare research platform, which is designed to facilitate translational research on intelligent diagnosis and treatment planning in various clinical scenarios.

Human-Computer Interaction Image and Video Processing

CFEA: Collaborative Feature Ensembling Adaptation for Domain Adaptation in Unsupervised Optic Disc and Cup Segmentation

2 code implementations16 Oct 2019 Peng Liu, Bin Kong, Zhongyu Li, Shaoting Zhang, Ruogu Fang

Our proposed CFEA is an interactive paradigm which presents an exquisite of collaborative adaptation through both adversarial learning and ensembling weights.

Unsupervised Domain Adaptation

Collaborative Multi-agent Learning for MR Knee Articular Cartilage Segmentation

no code implementations13 Aug 2019 Chaowei Tan, Zhennan Yan, Shaoting Zhang, Kang Li, Dimitris N. Metaxas

However, effective and efficient delineation of all the knee articular cartilages in large-sized and high-resolution 3D MR knee data is still an open challenge.

Decision Making Segmentation

Automatic Health Problem Detection from Gait Videos Using Deep Neural Networks

1 code implementation4 Jun 2019 Rahil Mehrizi, Xi Peng, Shaoting Zhang, Ruisong Liao, Kang Li

This study presents a starting point toward a powerful tool for automatic classification of gait disorders and can be used as a basis for future applications of Deep Learning in clinical gait analysis.

Feature Engineering General Classification +2

Attention-driven Tree-structured Convolutional LSTM for High Dimensional Data Understanding

no code implementations29 Jan 2019 Bin Kong, Xin Wang, Junjie Bai, Yi Lu, Feng Gao, Kunlin Cao, Qi Song, Shaoting Zhang, Siwei Lyu, Youbing Yin

In order to address these limitations, we present tree-structured ConvLSTM models for tree-structured image analysis tasks which can be trained end-to-end.

Vocal Bursts Intensity Prediction

Residual Attention based Network for Hand Bone Age Assessment

no code implementations21 Dec 2018 Eric Wu, Bin Kong, Xin Wang, Junjie Bai, Yi Lu, Feng Gao, Shaoting Zhang, Kunlin Cao, Qi Song, Siwei Lyu, Youbing Yin

The hierarchical attention components of the residual attention subnet force our network to focus on the key components of the X-ray images and generate the final predictions as well as the associated visual supports, which is similar to the assessment procedure of clinicians.

Hand Segmentation

Quantized Densely Connected U-Nets for Efficient Landmark Localization

1 code implementation ECCV 2018 Zhiqiang Tang, Xi Peng, Shijie Geng, Lingfei Wu, Shaoting Zhang, Dimitris Metaxas

Finally, to reduce the memory consumption and high precision operations both in training and testing, we further quantize weights, inputs, and gradients of our localization network to low bit-width numbers.

Face Alignment Pose Estimation

CR-GAN: Learning Complete Representations for Multi-view Generation

1 code implementation28 Jun 2018 Yu Tian, Xi Peng, Long Zhao, Shaoting Zhang, Dimitris N. Metaxas

Generating multi-view images from a single-view input is an essential yet challenging problem.

Self-Supervised Learning

Interactive Reinforcement Learning for Object Grounding via Self-Talking

no code implementations2 Dec 2017 Yan Zhu, Shaoting Zhang, Dimitris Metaxas

In this paper, we introduce an interactive training method to improve the natural language conversation system for a visual grounding task.

reinforcement-learning Reinforcement Learning (RL) +1

StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks

16 code implementations19 Oct 2017 Han Zhang, Tao Xu, Hongsheng Li, Shaoting Zhang, Xiaogang Wang, Xiaolei Huang, Dimitris Metaxas

In this paper, we propose Stacked Generative Adversarial Networks (StackGAN) aiming at generating high-resolution photo-realistic images.

StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks

21 code implementations ICCV 2017 Han Zhang, Tao Xu, Hongsheng Li, Shaoting Zhang, Xiaogang Wang, Xiaolei Huang, Dimitris Metaxas

Synthesizing high-quality images from text descriptions is a challenging problem in computer vision and has many practical applications.

Ranked #3 on Text-to-Image Generation on Oxford 102 Flowers (Inception score metric)

Multispectral Deep Neural Networks for Pedestrian Detection

2 code implementations8 Nov 2016 Jingjing Liu, Shaoting Zhang, Shu Wang, Dimitris N. Metaxas

Multispectral pedestrian detection is essential for around-the-clock applications, e. g., surveillance and autonomous driving.

Pedestrian Detection

Visual Tracking via Reliable Memories

no code implementations4 Feb 2016 Shu Wang, Shaoting Zhang, Wei Liu, Dimitris N. Metaxas

In this paper, we propose a novel visual tracking framework that intelligently discovers reliable patterns from a wide range of video to resist drift error for long-term tracking tasks.

Clustering Visual Tracking

Embedding Label Structures for Fine-Grained Feature Representation

no code implementations CVPR 2016 Xiaofan Zhang, Feng Zhou, Yuanqing Lin, Shaoting Zhang

However, previous studies have rarely focused on learning a fined-grained and structured feature representation that is able to locate similar images at different levels of relevance, e. g., discovering cars from the same make or the same model, both of which require high precision.

Fine-Grained Image Classification General Classification +3

Learning Binary Codes for Maximum Inner Product Search

no code implementations ICCV 2015 Fumin Shen, Wei Liu, Shaoting Zhang, Yang Yang, Heng Tao Shen

Inspired by the latest advance in asymmetric hashing schemes, we propose an asymmetric binary code learning framework based on inner product fitting.

PIEFA: Personalized Incremental and Ensemble Face Alignment

no code implementations ICCV 2015 Xi Peng, Shaoting Zhang, Yu Yang, Dimitris N. Metaxas

Face alignment, especially on real-time or large-scale sequential images, is a challenging task with broad applications.

Face Alignment Incremental Learning

Cannot find the paper you are looking for? You can Submit a new open access paper.