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Found 6 papers, 2 papers with code
Robust Tumor Segmentation with Hyperspectral Imaging and Graph Neural Networks
Despite the limited dataset, the GNN-based model significantly outperforms context-agnostic approaches, accurately distinguishing between healthy and tumor tissues, even in images from previously unseen patients.
Dynamic Scene Graph Representation for Surgical Video
Surgical videos captured from microscopic or endoscopic imaging devices are rich but complex sources of information, depicting different tools and anatomical structures utilized during an extended amount of time.
Whether and When does Endoscopy Domain Pretraining Make Sense?
In this work, we investigate the need for endoscopy domain-specific pretraining based on downstream objectives.
LABRAD-OR: Lightweight Memory Scene Graphs for Accurate Bimodal Reasoning in Dynamic Operating Rooms
The holistic representation of surgical scenes as semantic scene graphs (SGG), where entities are represented as nodes and relations between them as edges, is a promising direction for fine-grained semantic OR understanding.
Ranked #3 on
Scene Graph Generation
on 4D-OR
Location-Free Scene Graph Generation
Although using significantly fewer labels during training, we achieve 74. 12\% of the location-supervised SOTA performance on Visual Genome and even outperform the best method on 4D-OR.
Ranked #2 on
Scene Graph Generation
on 4D-OR
CholecTriplet2022: Show me a tool and tell me the triplet -- an endoscopic vision challenge for surgical action triplet detection
This paper presents the CholecTriplet2022 challenge, which extends surgical action triplet modeling from recognition to detection.
Ranked #1 on
Action Triplet Detection
on CholecT50 (Challenge)
