61 citations · 146 across the 40 of their papers we have counts for
32 papers · 1 filter
ESRVS: Extreme Semi-Supervised Retinal Vessel Segmentation with a Single Annotated Image
Mingzhi Xu, Yizhe Zhang
Learning from minimal human supervision is a long-standing goal in medical image analysis, where dense expert annotations are costly. We study retinal vessel segmentation in an ext…
SemiGDA: Generative Dual-distribution Alignment for Semi-Supervised Medical Image Segmentation
Kaiwen Huang, Yi Zhou, Yizhe Zhang +2
Semi-supervised learning addresses label scarcity and high annotation costs in medical image segmentation by exploiting the latent information in unlabeled data to enhance model pe…
Enhancing Medical Visual Grounding via Knowledge-guided Spatial Prompts
Yifan Gao, Tao Zhou, Yi Zhou +3
Medical Visual Grounding (MVG) aims to identify diagnostically relevant phrases from free-text radiology reports and localize their corresponding regions in medical images, providi…
Hierarchical Vision-Language Interaction for Facial Action Unit Detection
Yong Li, Yi Ren, Yizhe Zhang +5
Facial Action Unit (AU) detection seeks to recognize subtle facial muscle activations as defined by the Facial Action Coding System (FACS). A primary challenge w.r.t AU detection i…
Bidirectional Channel-selective Semantic Interaction for Semi-Supervised Medical Segmentation
Kaiwen Huang, Yizhe Zhang, Yi Zhou +2
Semi-supervised medical image segmentation is an effective method for addressing scenarios with limited labeled data. Existing methods mainly rely on frameworks such as mean teache…
Cell Instance Segmentation: The Devil Is in the Boundaries
Peixian Liang, Yifan Ding, Yizhe Zhang +9
State-of-the-art (SOTA) methods for cell instance segmentation are based on deep learning (DL) semantic segmentation approaches, focusing on distinguishing foreground pixels from b…
Hierarchical Spatio-temporal Segmentation Network for Ejection Fraction Estimation in Echocardiography Videos
Dongfang Wang, Jian Yang, Yizhe Zhang +1
Automated segmentation of the left ventricular endocardium in echocardiography videos is a key research area in cardiology. It aims to provide accurate assessment of cardiac struct…
Uncertainty-aware Cross-training for Semi-supervised Medical Image Segmentation
Kaiwen Huang, Tao Zhou, Huazhu Fu +3
Semi-supervised learning has gained considerable popularity in medical image segmentation tasks due to its capability to reduce reliance on expert-examined annotations. Several mea…
Decoupling Continual Semantic Segmentation
Yifu Guo, Yuquan Lu, Wentao Zhang +5
Continual Semantic Segmentation (CSS) requires learning new classes without forgetting previously acquired knowledge, addressing the fundamental challenge of catastrophic forgettin…
Text-driven Multiplanar Visual Interaction for Semi-supervised Medical Image Segmentation
Kaiwen Huang, Yi Zhou, Huazhu Fu +3
Semi-supervised medical image segmentation is a crucial technique for alleviating the high cost of data annotation. When labeled data is limited, textual information can provide ad…
Spatial Lifting for Dense Prediction
Mingzhi Xu, Yizhe Zhang
We present Spatial Lifting (SL), a novel methodology for dense prediction tasks. SL operates by lifting standard inputs, such as 2D images, into a higher-dimensional space and subs…
Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation
Kaiwen Huang, Tao Zhou, Huazhu Fu +4
The limited availability of labeled data has driven advancements in semi-supervised learning for medical image segmentation. Modern large-scale models tailored for general segmenta…
Adaptify: A Refined Adaptation Scheme for Frame Classification in Atrophic Gastritis Videos
Zinan Xiong, Shuijiao Chen, Yizhe Zhang +3
Atrophic gastritis is a significant risk factor for developing gastric cancer. The incorporation of machine learning algorithms can efficiently elevate the possibility of accuratel…
EndoFinder: Online Image Retrieval for Explainable Colorectal Polyp Diagnosis
Ruijie Yang, Yan Zhu, Peiyao Fu +6
Determining the necessity of resecting malignant polyps during colonoscopy screen is crucial for patient outcomes, yet challenging due to the time-consuming and costly nature of hi…
Boosting Medical Image Classification with Segmentation Foundation Model
Pengfei Gu, Zihan Zhao, Hongxiao Wang +5
The Segment Anything Model (SAM) exhibits impressive capabilities in zero-shot segmentation for natural images. Recently, SAM has gained a great deal of attention for its applicati…
Improving Segment Anything on the Fly: Auxiliary Online Learning and Adaptive Fusion for Medical Image Segmentation
Tianyu Huang, Tao Zhou, Weidi Xie +3
The current variants of the Segment Anything Model (SAM), which include the original SAM and Medical SAM, still lack the capability to produce sufficiently accurate segmentation fo…
Test-time generative augmentation for medical image segmentation
Xiao Ma, Yuhui Tao, Zetian Zhang +7
Medical image segmentation is critical for clinical diagnosis, treatment planning, and monitoring, yet segmentation models often struggle with uncertainties stemming from occlusion…
Dual-scale Enhanced and Cross-generative Consistency Learning for Semi-supervised Medical Image Segmentation
Yunqi Gu, Tao Zhou, Yizhe Zhang +4
Medical image segmentation plays a crucial role in computer-aided diagnosis. However, existing methods heavily rely on fully supervised training, which requires a large amount of l…
Segment Anything Model-guided Collaborative Learning Network for Scribble-supervised Polyp Segmentation
Yiming Zhao, Tao Zhou, Yunqi Gu +4
Polyp segmentation plays a vital role in accurately locating polyps at an early stage, which holds significant clinical importance for the prevention of colorectal cancer. Various…
A Survey on Deep Learning for Polyp Segmentation: Techniques, Challenges and Future Trends
Jiaxin Mei, Tao Zhou, Kaiwen Huang +4
Early detection and assessment of polyps play a crucial role in the prevention and treatment of colorectal cancer (CRC). Polyp segmentation provides an effective solution to assist…
Knowledge Extraction and Distillation from Large-Scale Image-Text Colonoscopy Records Leveraging Large Language and Vision Models
Shuo Wang, Yan Zhu, Xiaoyuan Luo +8
The development of artificial intelligence systems for colonoscopy analysis often necessitates expert-annotated image datasets. However, limitations in dataset size and diversity i…
Input Augmentation with SAM: Boosting Medical Image Segmentation with Segmentation Foundation Model
Yizhe Zhang, Tao Zhou, Shuo Wang +2
The Segment Anything Model (SAM) is a recently developed large model for general-purpose segmentation for computer vision tasks. SAM was trained using 11 million images with over 1…
GPT4MIA: Utilizing Generative Pre-trained Transformer (GPT-3) as A Plug-and-Play Transductive Model for Medical Image Analysis
Yizhe Zhang, Danny Z. Chen
In this paper, we propose a novel approach (called GPT4MIA) that utilizes Generative Pre-trained Transformer (GPT) as a plug-and-play transductive inference tool for medical image…
Data-Driven Deep Supervision for Skin Lesion Classification
Suraj Mishra, Yizhe Zhang, Li Zhang +3
Automatic classification of pigmented, non-pigmented, and depigmented non-melanocytic skin lesions have garnered lots of attention in recent years. However, imaging variations in s…