计算机科学
人工智能
知识图
理解力
图形
机器学习
数据科学
理论计算机科学
程序设计语言
作者
Yutong Ban,Jennifer A. Eckhoff,Thomas Ward,Daniel A. Hashimoto,Ozanan R. Meireles,Daniela Rus,Guy Rosman
出处
期刊:IEEE Transactions on Medical Imaging
[Institute of Electrical and Electronics Engineers]
日期:2024-01-01
卷期号:43 (1): 264-274
被引量:1
标识
DOI:10.1109/tmi.2023.3299518
摘要
Analysis of relations between objects and comprehension of abstract concepts in the surgical video is important in AI-augmented surgery. Yet, building models that integrate our knowledge and understanding of surgery remains a challenging endeavor. In this paper, we propose a novel way to integrate conceptual knowledge into temporal analysis tasks using temporal concept graph networks. In the proposed networks, a knowledge graph is incorporated into the temporal video analysis of surgical notions, learning the meaning of concepts and relations as they apply to the data. We demonstrate results in surgical video data for tasks such as verification of the critical view of safety, estimation of the Parkland grading scale as well as recognizing instrument-action-tissue triplets. The results show that our method improves the recognition and detection of complex benchmarks as well as enables other analytic applications of interest.
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