Search Results for author: Shaoting Zhang

Found 47 papers, 26 papers with code

Contrastive Domain Disentanglement for Generalizable Medical Image Segmentation

no code implementations13 May 2022 Ran Gu, Jiangshan Lu, Jingyang Zhang, Wenhui Lei, Xiaofan Zhang, Guotai Wang, Shaoting Zhang

To tackle this deficiency, we propose Contrastive Domain Disentangle (CDD) network for generalizable medical image segmentation.

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.

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.

Semantic Segmentation Semi-supervised Medical Image Segmentation

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 Medical Image Segmentation +2

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

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 Medical Image Segmentation +1

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.

Frame

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.

Image Classification Self-Supervised Learning +1

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 Semantic correspondence +1

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 online learning +2

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

1 code implementation25 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.

Interactive Segmentation Medical Image Segmentation +1

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)

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.

Medical Image Segmentation Semantic Segmentation

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 +1

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.

Medical Image Segmentation Semantic Segmentation

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

1 code implementation22 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.

Lesion Segmentation Semantic Segmentation +1

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.

Neural Architecture Search Semantic Segmentation +1

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.

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.

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 +1

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

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.

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 for smart healthcare, which is designed to boost 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

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.

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 Visual Grounding

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.

Text-to-Image Generation

Multispectral Deep Neural Networks for Pedestrian Detection

1 code implementation8 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.

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 +2

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 Frame +1

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