arXiv Computer Vision
Optimize Surgical Triplet Recognition: A Knowledge-Driven Mixture-of-Experts Solution
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arXiv AI
Jun 16
K-Prism: A Knowledge-Guided and Prompt Integrated Universal Medical Image Segmentation Model
arXiv:2509. 25594v2 Announce Type: replace-cross Abstract: Medical image segmentation is fundamental to clinical decision-making, yet existing models remain fragmented.
arXiv Computer Vision
Sep 30
UniMedSeg: Unified In-Context Learning for Multi-Paradigm 2D/3D Medical Image Segmentation
arXiv:2607.12896v3 Announce Type: replace Abstract: Medical image segmentation foundation models are expected to generalize across diverse clinical scenarios, yet existing universal methods remain fr...
arXiv AI
Sep 17
SurgRAW: Multi-Agent Workflow with Chain of Thought Reasoning for Robotic Surgical Video Analysis
SurgRAW introduces a multi‑agent, chain‑of‑thought workflow for robotic surgical video analysis, leveraging a new SurgCoTBench benchmark with 14,256 QA pairs across five surgical tasks. The system uses an orchestrator to split scene understanding into two reasoning streams, panel‑discussion‑style collaboration among task‑specific agents, and retrieval‑augmented generation to incorporate surgical knowledge. Experiments show SurgRAW outperforms mainstream vision‑language models and a supervised baseline by 14.61% accuracy.
Hugging Face Trending Papers
Jul 21
Advancing Multimodal Fusion on Heterogeneous Medical Data with Hybrid Geometry Attention
Multimodal fusion learning (MFL) has shown great potential in the medical domain, where we are faced with disparate data modalities such as imaging, clinical records, and omics. However, existing MFL strategies face several major challenges.
arXiv Computer Vision
Sep 21
SIRA: Reasoning-Aware Surgical Instrument Segmentation via Query-Anchored Alignment
arXiv:2609.21402v1 Announce Type: new Abstract: Surgical instrument segmentation (SIS) plays a critical role in robotic assistance and surgical workflow analysis. However, most existing SIS methods f...
arXiv Computer Vision
Sep 4
SurgAtlas: A Large-Scale Surgical Video-Language Dataset with 2,391 Hours of Open and Minimally Invasive Surgery
SurgAtlas is the largest surgical video‑language dataset, containing 15,291 videos (2,391 hours) across 18 specialties and over 5,000 procedure types, all sourced from public YouTube. It uniquely includes open‑surgery videos at scale (6,182) alongside more than 9,000 minimally invasive recordings, and introduces standardized benchmarks for open‑surgery video understanding. The dataset offers a rich, multi‑tier annotation schema—segment‑level captions, step/phase descriptions, video‑level surgical narratives, and reasoning‑oriented VQA pairs—validated by experts and built through an automated LLM‑enriched pipeline. "whyItMatters":"SurgAtlas provides an unprecedentedly large, diverse, and clinically validated resource that can train and benchmark multimodal surgical AI models, advancing the development of next‑generation foundation models for surgery."