61 citations · 102 across the 21 of their papers we have counts for
21 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…
Text-Driven Fusion for Infrared and Visible Images: Achieving Image Scene Adaptation on Hyperbolic Space
Huan Kang, Hui Li, Tianyang Xu +3
Infrared and visible image fusion aims to integrate complementary modalities, while existing Euclidean methods impose rigid distance metrics that distort multi-modal interactions a…
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…
Language as an Anchor: Preserving Relative Visual Geometry for Domain Incremental Learning
Shuyi Geng, Tao Zhou, Yi Zhou
A key challenge in Domain Incremental Learning (DIL) is to continually learn under shifting distributions while preserving knowledge from previous domains. Existing methods face a…
Bridging the Gap in Ophthalmic AI: MM-Retinal-Reason Dataset and OphthaReason Model toward Dynamic Multimodal Reasoning
Ruiqi Wu, Yuang Yao, Tengfei Ma +11
Multimodal large language models (MLLMs) have recently demonstrated remarkable reasoning abilities under reinforcement learning (RL) paradigm. However, most existing multimodal med…
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…
Continual Retinal Vision-Language Pre-training upon Incremental Imaging Modalities
Yuang Yao, Ruiqi Wu, Yi Zhou +1
Traditional fundus image analysis models focus on single-modal tasks, ignoring fundus modality complementarity, which limits their versatility. Recently, retinal foundation models…
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…
MM-Retinal V2: Transfer an Elite Knowledge Spark into Fundus Vision-Language Pretraining
Ruiqi Wu, Na Su, Chenran Zhang +10
Vision-language pretraining (VLP) has been investigated to generalize across diverse downstream tasks for fundus image analysis. Although recent methods showcase promising achievem…
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…
Exploring the Interplay Between Video Generation and World Models in Autonomous Driving: A Survey
Ao Fu, Yi Zhou, Tao Zhou +5
World models and video generation are pivotal technologies in the domain of autonomous driving, each playing a critical role in enhancing the robustness and reliability of autonomo…
SimTxtSeg: Weakly-Supervised Medical Image Segmentation with Simple Text Cues
Yuxin Xie, Tao Zhou, Yi Zhou +1
Weakly-supervised medical image segmentation is a challenging task that aims to reduce the annotation cost while keep the segmentation performance. In this paper, we present a nove…
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…
SQA-SAM: Segmentation Quality Assessment for Medical Images Utilizing the Segment Anything Model
Yizhe Zhang, Shuo Wang, Tao Zhou +2
Segmentation quality assessment (SQA) plays a critical role in the deployment of a medical image based AI system. Users need to be informed/alerted whenever an AI system generates…
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…