61 citations · 146 across the 40 of their papers we have counts for
41 papers
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…
Can General-Purpose Omnimodels Compete with Specialists? A Case Study in Medical Image Segmentation
Yizhe Zhang, Qiang Chen, Tao Zhou
The emergence of powerful, general-purpose omnimodels capable of processing diverse data modalities has raised a critical question: can these ``jack-of-all-trades'' systems perform…
A Closer Look at Edema Area Segmentation in SD-OCT Images Using Adversarial Framework
Yuhui Tao, Yizhe Zhang, Qiang Chen
The development of artificial intelligence models for macular edema (ME) analy-sis always relies on expert-annotated pixel-level image datasets which are expen-sive to collect pros…
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…
Beyond Manual Annotation: A Human-AI Collaborative Framework for Medical Image Segmentation Using Only "Better or Worse" Expert Feedback
Yizhe Zhang
Manual annotation of medical images is a labor-intensive and time-consuming process, posing a significant bottleneck in the development and deployment of robust medical imaging AI…
EndoFinder: Online Lesion Retrieval for Explainable Colorectal Polyp Diagnosis Leveraging Latent Scene Representations
Ruijie Yang, Yan Zhu, Peiyao Fu +6
Colorectal cancer (CRC) remains a leading cause of cancer-related mortality, underscoring the importance of timely polyp detection and diagnosis. While deep learning models have im…
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…
TSBP: Improving Object Detection in Histology Images via Test-time Self-guided Bounding-box Propagation
Tingting Yang, Liang Xiao, Yizhe Zhang
A global threshold (e.g., 0.5) is often applied to determine which bounding boxes should be included in the final results for an object detection task. A higher threshold reduces f…
Towards Ground-truth-free Evaluation of Any Segmentation in Medical Images
Ahjol Senbi, Tianyu Huang, Fei Lyu +8
We explore the feasibility and potential of building a ground-truth-free evaluation model to assess the quality of segmentations generated by the Segment Anything Model (SAM) and i…
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…
An Empirical Study on the Fairness of Foundation Models for Multi-Organ Image Segmentation
Qin Li, Yizhe Zhang, Yan Li +11
The segmentation foundation model, e.g., Segment Anything Model (SAM), has attracted increasing interest in the medical image community. Early pioneering studies primarily concentr…
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…