242 citations · 414 across the 22 of their papers we have counts for
22 papers
Beyond Visual Cues: CoT-Enhanced Reasoning for Semi-supervised Medical Image Segmentation
Yuming Chen, Yuxin Xie, Tao Zhou +1
Semi-supervised medical image segmentation has emerged as a dominant research problem in medical image analysis, mitigating annotation scarcity by leveraging consistency regulariza…
Propagating Structural Guidance: Synthesizing Fluorescein Angiography from Fundus Images and Sparse OCT Scans
Tengfei Ma, Ruiqi Wu, Chenran Zhang +6
Fundus fluorescein angiography (FFA) is critical for assessing retinal vascular abnormalities, but its acquisition is invasive and not always feasible. In contrast, color fundus ph…
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…
MedKCO: Medical Vision-Language Pretraining via Knowledge-Driven Cognitive Orchestration
Chenran Zhang, Ruiqi Wu, Tao Zhou +1
Medical vision-language pretraining (VLP) models have recently been investigated for their generalization to diverse downstream tasks. However, current medical VLP methods typicall…
CORE-Seg: Reasoning-Driven Segmentation for Complex Lesions via Reinforcement Learning
Yuxin Xie, Yuming Chen, Yishan Yang +5
Medical image segmentation is undergoing a paradigm shift from conventional visual pattern matching to cognitive reasoning analysis. Although Multimodal Large Language Models (MLLM…
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…
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…
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…
Universal Incremental Learning: Mitigating Confusion from Inter- and Intra-task Distribution Randomness
Sheng Luo, Yi Zhou, Jinsong Shi +1
Existing incremental learning (IL) methods rely on strong assumptions: incoming tasks either introduce exclusively new classes or new domains (i.e., Class IL and Domain IL), or inc…
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…
MM-Retinal: Knowledge-Enhanced Foundational Pretraining with Fundus Image-Text Expertise
Ruiqi Wu, Chenran Zhang, Jianle Zhang +3
Current fundus image analysis models are predominantly built for specific tasks relying on individual datasets. The learning process is usually based on data-driven paradigm withou…
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…
Memory-Assisted Sub-Prototype Mining for Universal Domain Adaptation
Yuxiang Lai, Yi Zhou, Xinghong Liu +1
Universal domain adaptation aims to align the classes and reduce the feature gap between the same category of the source and target domains. The target private category is set as t…
COCA: Classifier-Oriented Calibration via Textual Prototype for Source-Free Universal Domain Adaptation
Xinghong Liu, Yi Zhou, Tao Zhou +2
Universal domain adaptation (UniDA) aims to address domain and category shifts across data sources. Recently, due to more stringent data restrictions, researchers have introduced s…
Can SAM Segment Polyps?
Tao Zhou, Yizhe Zhang, Yi Zhou +2
Recently, Meta AI Research releases a general Segment Anything Model (SAM), which has demonstrated promising performance in several segmentation tasks. As we know, polyp segmentati…
Self-Paced Learning for Open-Set Domain Adaptation
Xinghong Liu, Yi Zhou, Tao Zhou +2
Domain adaptation tackles the challenge of generalizing knowledge acquired from a source domain to a target domain with different data distributions. Traditional domain adaptation…
Feature Aggregation and Propagation Network for Camouflaged Object Detection
Tao Zhou, Yi Zhou, Chen Gong +2
Camouflaged object detection (COD) aims to detect/segment camouflaged objects embedded in the environment, which has attracted increasing attention over the past decades. Although…
Multi-scale Transformer Network with Edge-aware Pre-training for Cross-Modality MR Image Synthesis
Yonghao Li, Tao Zhou, Kelei He +2
Cross-modality magnetic resonance (MR) image synthesis can be used to generate missing modalities from given ones. Existing (supervised learning) methods often require a large numb…
Exploring the Hierarchy in Relation Labels for Scene Graph Generation
Yi Zhou, Shuyang Sun, Chao Zhang +2
By assigning each relationship a single label, current approaches formulate the relationship detection as a classification problem. Under this formulation, predicate categories are…