A hierarchical prompt tree with self-reflection graph propagation enables positive forward and backward knowledge transfer in incremental surgical instrument segmentation, improving over baselines by more than 5% and 11% on two benchmarks.
2018 robotic scene segmentation challenge
6 Pith papers cite this work. Polarity classification is still indexing.
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Introduces the first publicly accessible native 4K resolution endoscopic video dataset for robotic-assisted minimally invasive procedures.
RoboSurg-VQA is a new segmentation-aware VQA benchmark created by repurposing public surgical datasets with fixed clinically motivated questions and closed answer sets.
SAM 2 delivers more accurate video segmentation with 3x fewer user interactions and 6x faster image segmentation than the original SAM by training a streaming-memory transformer on the largest video segmentation dataset collected to date.
Releases the SurgVU dataset of surgical videos and labels to enable machine learning research in surgical data science.
SegSTRONG-C provides a new benchmark where top models reach 0.9394 DSC and 0.9301 NSD on corrupted surgical tool segmentation tests, showing conventional techniques help but calling for more innovative robustness methods.
citing papers explorer
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Unlocking Positive Transfer in Incrementally Learning Surgical Instruments: A Self-reflection Hierarchical Prompt Framework
A hierarchical prompt tree with self-reflection graph propagation enables positive forward and backward knowledge transfer in incremental surgical instrument segmentation, improving over baselines by more than 5% and 11% on two benchmarks.
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SurgiSR4K: A High-Resolution Endoscopic Video Dataset for Robotic-Assisted Minimally Invasive Procedures
Introduces the first publicly accessible native 4K resolution endoscopic video dataset for robotic-assisted minimally invasive procedures.
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RoboSurg-VQA: A Multimodal Benchmark for Surgical Segmentation-Aware Visual Question Answering
RoboSurg-VQA is a new segmentation-aware VQA benchmark created by repurposing public surgical datasets with fixed clinically motivated questions and closed answer sets.
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SAM 2: Segment Anything in Images and Videos
SAM 2 delivers more accurate video segmentation with 3x fewer user interactions and 6x faster image segmentation than the original SAM by training a streaming-memory transformer on the largest video segmentation dataset collected to date.
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Surgical Visual Understanding (SurgVU) Dataset
Releases the SurgVU dataset of surgical videos and labels to enable machine learning research in surgical data science.
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SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge
SegSTRONG-C provides a new benchmark where top models reach 0.9394 DSC and 0.9301 NSD on corrupted surgical tool segmentation tests, showing conventional techniques help but calling for more innovative robustness methods.