2024-01-23-hamoud24a.md

title	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
ST(OR)$^2$: Spatio-Temporal Object Level Reasoning for Activity Recognition in the Operating Room	Surgical robotics holds much promise for improving patient safety and clinician experience in the Operating Room (OR). However, it also comes with new challenges, requiring strong team coordination and effective OR management. Automatic detection of surgical activities is a key requirement for developing AI-based intelligent tools to tackle these challenges. The current state-of-the-art surgical activity recognition methods however operate on image-based representations and depend on large-scale labeled datasets whose collection is time-consuming and resource-expensive. This work proposes a new sample-efficient and object-based approach for surgical activity recognition in the OR. Our method focuses on the geometric arrangements between clinicians and surgical devices, thus utilizing the significant object interaction dynamics in the OR. We conduct experiments in a low-data regime study for long video activity recognition. We also benchmark our method against other object-centric approaches on clip-level action classification and show superior performance.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	hamoud24a	0	ST(OR)$^2$: Spatio-Temporal Object Level Reasoning for Activity Recognition in the Operating Room	1254	1268	1254-1268	1254	false	Hamoud, Idris and Jamal, Muhammad Abdullah and Srivastav, Vinkle and MUTTER, Didier and Padoy, Nicolas and Mohareri, Omid	given family Idris Hamoud given family Muhammad Abdullah Jamal given family Vinkle Srivastav given family Didier MUTTER given family Nicolas Padoy given family Omid Mohareri	2024-01-23		Medical Imaging with Deep Learning	227	inproceedings	date-parts 2024 1 23	https://proceedings.mlr.press/v227/hamoud24a/hamoud24a.pdf