6 papers
Synergistic Perception-Reasoning Governance: Grounding Medical MLLMs with Verifiable Anatomical Evidence
Rui Hao, Qiankun Li, Junyuan Mao +4
Multimodal large language models (MLLMs) show strong promise for clinical VQA and radiology report generation, yet inference-time hallucinations still undermine trustworthy use: mo…
Multi-scale Cascaded Foundation Model for Whole-body Organs-at-risk Segmentation
Rui Hao, Dayu Tan, Qiankun Li +3
Accurate segmentation of organs-at-risk (OARs) is vital for safe and precise radiotherapy and surgery. Most existing studies segment only a limited set of organs or regions, lackin…
An Enhanced Pyramid Feature Network Based on Long-Range Dependencies for Multi-Organ Medical Image Segmentation
Dayu Tan, Cheng Kong, Yansen Su +4
In the field of multi-organ medical image segmentation, recent methods frequently employ Transformers to capture long-range dependencies from image features. However, these methods…
MSD-KMamba: Bidirectional Spatial-Aware Multi-Modal 3D Brain Segmentation via Multi-scale Self-Distilled Fusion Strategy
Dayu Tan, Ziwei Zhang, Yansan Su +4
Numerous CNN-Transformer hybrid models rely on high-complexity global attention mechanisms to capture long-range dependencies, which introduces non-linear computational complexity…
HiPerformer: A High-Performance Global-Local Segmentation Model with Modular Hierarchical Fusion Strategy
Dayu Tan, Zhenpeng Xu, Yansen Su +3
Both local details and global context are crucial in medical image segmentation, and effectively integrating them is essential for achieving high accuracy. However, existing mainst…
PGCLODA: Prompt-Guided Graph Contrastive Learning for Oligopeptide-Infectious Disease Association Prediction
Dayu Tan, Jing Chen, Xiaoping Zhou +2
Infectious diseases continue to pose a serious threat to public health, underscoring the urgent need for effective computational approaches to screen novel anti-infective agents. O…