12 papers
Multimodal Causal-Driven Representation Learning for Generalizable Medical Image Segmentation
Xusheng Liang, Lihua Zhou, Nianxin Li +8
Vision-Language Models (VLMs), such as CLIP, have demonstrated remarkable zero-shot capabilities in various computer vision tasks. However, their application to medical imaging rem…
MAT-Cell: A Multi-Agent Tree-Structured Reasoning Framework for Batch-Level Single-Cell Annotation
Yehui Yang, Zelin Zang, Xienan Zheng +9
Automated single-cell annotation is difficult when the most abundant genes are not the most discriminative ones, or when a target state is poorly covered by a fixed reference atlas…
SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos
Jinlin Wu, Felix Holm, Chuxi Chen +17
While foundation models have advanced surgical video analysis, current approaches rely predominantly on pixel-level reconstruction objectives that waste model capacity on low-level…
Anatomy-R1: Enhancing Anatomy Reasoning in Multimodal Large Language Models via Anatomical Similarity Curriculum and Group Diversity Augmentation
Ziyang Song, Zelin Zang, Zuyao Chen +6
Multimodal Large Language Models (MLLMs) have achieved impressive progress in natural image reasoning, yet their potential in medical imaging remains underexplored, especially in c…
NeuroABench: A Multimodal Evaluation Benchmark for Neurosurgical Anatomy Identification
Ziyang Song, Zelin Zang, Xiaofan Ye +7
Multimodal Large Language Models (MLLMs) have shown significant potential in surgical video understanding. With improved zero-shot performance and more effective human-machine inte…
Learning Cell-Aware Hierarchical Multi-Modal Representations for Robust Molecular Modeling
Mengran Li, Zelin Zang, Wenbin Xing +4
Understanding how chemical perturbations propagate through biological systems is essential for robust molecular property prediction. While most existing methods focus on chemical s…