Deep learning approaches to surgical video segmentation and object detection: A scoping review.

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Bibliographic Details
Title: Deep learning approaches to surgical video segmentation and object detection: A scoping review.
Authors: Kamtam DN; Division of Thoracic Surgery, Department of Cardiothoracic Surgery, Stanford University School of Medicine, Stanford, CA, USA. Electronic address: devanish@stanford.edu., Shrager JB; Division of Thoracic Surgery, Department of Cardiothoracic Surgery, Stanford University School of Medicine, Stanford, CA, USA; Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA., Malla SD; Division of Thoracic Surgery, Department of Cardiothoracic Surgery, Stanford University School of Medicine, Stanford, CA, USA., Lin N; Division of Thoracic Surgery, Department of Cardiothoracic Surgery, Stanford University School of Medicine, Stanford, CA, USA., Cardona JJ; Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA., Kim JJ; Division of Thoracic Surgery, Department of Cardiothoracic Surgery, Stanford University School of Medicine, Stanford, CA, USA., Hu C; Hotpot.ai, Palo Alto, CA, USA.
Source: Computers in biology and medicine [Comput Biol Med] 2025 Aug; Vol. 194, pp. 110482. Date of Electronic Publication: 2025 Jun 02.
Publication Type: Journal Article; Scoping Review
Journal Info: Publisher: Elsevier Country of Publication: United States NLM ID: 1250250 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-0534 (Electronic) Linking ISSN: 00104825 NLM ISO Abbreviation: Comput Biol Med Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1879-0534
DOI:10.1016/j.compbiomed.2025.110482