activity
20242026
collaborators

6 papers

cs.CV2026

IBISAgent: Reinforcing Pixel-Level Visual Reasoning in MLLMs for Universal Biomedical Object Referring and Segmentation

Yankai Jiang, Qiaoru Li, Binlu Xu +6

Recent research on medical MLLMs has gradually shifted its focus from image-level understanding to fine-grained, pixel-level comprehension. Although segmentation serves as the foun…

cs.CV2025

ROFI: A Deep Learning-Based Ophthalmic Sign-Preserving and Reversible Patient Face Anonymizer

Yuan Tian, Min Zhou, Yitong Chen +19

Patient face images provide a convenient mean for evaluating eye diseases, while also raising privacy concerns. Here, we introduce ROFI, a deep learning-based privacy protection fr…

cs.CV2025

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models

Shizhan Gong, Yankai Jiang, Qi Dou +1

Vision-language models, such as CLIP, have achieved significant success in aligning visual and textual representations, becoming essential components of many multi-modal large lang…

cs.CV2025

TK-Mamba: Marrying KAN With Mamba for Text-Driven 3D Medical Image Segmentation

Haoyu Yang, Yutong Guan, Meixing Shi +8

3D medical image segmentation is important for clinical diagnosis and treatment but faces challenges from high-dimensional data and complex spatial dependencies. Traditional single…

eess.IV2025

A Synthetic Data-Driven Radiology Foundation Model for Pan-tumor Clinical Diagnosis

Wenhui Lei, Hanyu Chen, Zitian Zhang +13

AI-assisted imaging made substantial advances in tumor diagnosis and management. However, a major barrier to developing robust oncology foundation models is the scarcity of large-s…

cs.CV2024

Unleashing the Potential of Vision-Language Pre-Training for 3D Zero-Shot Lesion Segmentation via Mask-Attribute Alignment

Yankai Jiang, Wenhui Lei, Xiaofan Zhang +1

Recent advancements in medical vision-language pre-training models have driven significant progress in zero-shot disease recognition. However, transferring image-level knowledge to…