342 citations · 480 across the 33 of their papers we have counts for
33 papers
Seg4Reg+: Consistency Learning between Spine Segmentation and Cobb Angle Regression
Yi Lin, Luyan Liu, Kai Ma +1
Automated methods for Cobb angle estimation are of high demand for scoliosis assessment. Existing methods typically calculate the Cobb angle from landmark estimation, or simply com…
DFTR: Depth-supervised Fusion Transformer for Salient Object Detection
Heqin Zhu, Xu Sun, Yuexiang Li +3
Automated salient object detection (SOD) plays an increasingly crucial role in many computer vision applications. By reformulating the depth information as supervision rather than…
Alleviating Noisy-label Effects in Image Classification via Probability Transition Matrix
Ziqi Zhang, Yuexiang Li, Hongxin Wei +3
Deep-learning-based image classification frameworks often suffer from the noisy label problem caused by the inter-observer variation. Recent studies employed learning-to-learn para…
Training Automatic View Planner for Cardiac MR Imaging via Self-Supervision by Spatial Relationship between Views
Dong Wei, Kai Ma, Yefeng Zheng
View planning for the acquisition of cardiac magnetic resonance imaging (CMR) requires acquaintance with the cardiac anatomy and remains a challenging task in clinical practice. Ex…
Mutual-GAN: Towards Unsupervised Cross-Weather Adaptation with Mutual Information Constraint
Jiawei Chen, Yuexiang Li, Kai Ma +1
Convolutional neural network (CNN) have proven its success for semantic segmentation, which is a core task of emerging industrial applications such as autonomous driving. However,…
Residual Moment Loss for Medical Image Segmentation
Quanziang Wang, Renzhen Wang, Yuexiang Li +3
Location information is proven to benefit the deep learning models on capturing the manifold structure of target objects, and accordingly boosts the accuracy of medical image segme…
Stabilized Medical Image Attacks
Gege Qi, Lijun Gong, Yibing Song +2
Convolutional Neural Networks (CNNs) have advanced existing medical systems for automatic disease diagnosis. However, a threat to these systems arises that adversarial attacks make…
MI^2GAN: Generative Adversarial Network for Medical Image Domain Adaptation using Mutual Information Constraint
Xinpeng Xie, Jiawei Chen, Yuexiang Li +3
Domain shift between medical images from multicentres is still an open question for the community, which degrades the generalization performance of deep learning models. Generative…
TR-GAN: Topology Ranking GAN with Triplet Loss for Retinal Artery/Vein Classification
Wenting Chen, Shuang Yu, Junde Wu +5
Retinal artery/vein (A/V) classification lays the foundation for the quantitative analysis of retinal vessels, which is associated with potential risks of various cardiovascular an…
Difficulty-aware Glaucoma Classification with Multi-Rater Consensus Modeling
Shuang Yu, Hong-Yu Zhou, Kai Ma +4
Medical images are generally labeled by multiple experts before the final ground-truth labels are determined. Consensus or disagreement among experts regarding individual images re…
Learning Crisp Edge Detector Using Logical Refinement Network
Luyan Liu, Kai Ma, Yefeng Zheng
Edge detection is a fundamental problem in different computer vision tasks. Recently, edge detection algorithms achieve satisfying improvement built upon deep learning. Although mo…
Leveraging Undiagnosed Data for Glaucoma Classification with Teacher-Student Learning
Junde Wu, Shuang Yu, Wenting Chen +5
Recently, deep learning has been adopted to the glaucoma classification task with performance comparable to that of human experts. However, a well trained deep learning model deman…
Instance-aware Self-supervised Learning for Nuclei Segmentation
Xinpeng Xie, Jiawei Chen, Yuexiang Li +3
Due to the wide existence and large morphological variances of nuclei, accurate nuclei instance segmentation is still one of the most challenging tasks in computational pathology.…
Comparing to Learn: Surpassing ImageNet Pretraining on Radiographs By Comparing Image Representations
Hong-Yu Zhou, Shuang Yu, Cheng Bian +3
In deep learning era, pretrained models play an important role in medical image analysis, in which ImageNet pretraining has been widely adopted as the best way. However, it is unde…
Distractor-Aware Neuron Intrinsic Learning for Generic 2D Medical Image Classifications
Lijun Gong, Kai Ma, Yefeng Zheng
Medical image analysis benefits Computer Aided Diagnosis (CADx). A fundamental analyzing approach is the classification of medical images, which serves for skin lesion diagnosis, d…
GREEN: a Graph REsidual rE-ranking Network for Grading Diabetic Retinopathy
Shaoteng Liu, Lijun Gong, Kai Ma +1
The automatic grading of diabetic retinopathy (DR) facilitates medical diagnosis for both patients and physicians. Existing researches formulate DR grading as an image classificati…
A Macro-Micro Weakly-supervised Framework for AS-OCT Tissue Segmentation
Munan Ning, Cheng Bian, Donghuan Lu +7
Primary angle closure glaucoma (PACG) is the leading cause of irreversible blindness among Asian people. Early detection of PACG is essential, so as to provide timely treatment and…
Deep Image Clustering with Category-Style Representation
Junjie Zhao, Donghuan Lu, Kai Ma +2
Deep clustering which adopts deep neural networks to obtain optimal representations for clustering has been widely studied recently. In this paper, we propose a novel deep image cl…
Self-Loop Uncertainty: A Novel Pseudo-Label for Semi-Supervised Medical Image Segmentation
Yuexiang Li, Jiawei Chen, Xinpeng Xie +2
Witnessing the success of deep learning neural networks in natural image processing, an increasing number of studies have been proposed to develop deep-learning-based frameworks fo…
Multi-Task Neural Networks with Spatial Activation for Retinal Vessel Segmentation and Artery/Vein Classification
Wenao Ma, Shuang Yu, Kai Ma +3
Retinal artery/vein (A/V) classification plays a critical role in the clinical biomarker study of how various systemic and cardiovascular diseases affect the retinal vessels. Conve…
Revisiting Rubik's Cube: Self-supervised Learning with Volume-wise Transformation for 3D Medical Image Segmentation
Xing Tao, Yuexiang Li, Wenhui Zhou +2
Deep learning highly relies on the quantity of annotated data. However, the annotations for 3D volumetric medical data require experienced physicians to spend hours or even days fo…
Learning and Exploiting Interclass Visual Correlations for Medical Image Classification
Dong Wei, Shilei Cao, Kai Ma +1
Deep neural network-based medical image classifications often use "hard" labels for training, where the probability of the correct category is 1 and those of others are 0. However,…
Cross-denoising Network against Corrupted Labels in Medical Image Segmentation with Domain Shift
Qinming Zhang, Luyan Liu, Kai Ma +2
Deep convolutional neural networks (DCNNs) have contributed many breakthroughs in segmentation tasks, especially in the field of medical imaging. However, \textit{domain shift} and…
Generative Adversarial Networks for Video-to-Video Domain Adaptation
Jiawei Chen, Yuexiang Li, Kai Ma +1
Endoscopic videos from multicentres often have different imaging conditions, e.g., color and illumination, which make the models trained on one domain usually fail to generalize we…