Robust multi-label surgical tool classification in noisy endoscopic videos

Qayyum, Adnan and Ali, Hassan and Caputo, Massimo and Vohra, Hunaid and Akinosho, Taofeek and Abioye, Sofiat and Berrou, Ilhem and Capik, Paweł and Qadir, Junaid and Bilal, Muhammad (2025) Robust multi-label surgical tool classification in noisy endoscopic videos. Scientific Reports, 15 (1). ISSN 2045-2322

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Abstract

Over the past few years, surgical data science has attracted substantial interest from the machine learning (ML) community. Various studies have demonstrated the efficacy of emerging ML techniques in analysing surgical data, particularly recordings of procedures, for digitising clinical and non-clinical functions like preoperative planning, context-aware decision-making, and operating skill assessment. However, this field is still in its infancy and lacks representative, well-annotated datasets for training robust models in intermediate ML tasks. Also, existing datasets suffer from inaccurate labels, hindering the development of reliable models. In this paper, we propose a systematic methodology for developing robust models for surgical tool classification using noisy endoscopic videos. Our methodology introduces two key innovations: (1) an intelligent active learning strategy for minimal dataset identification and label correction by human experts through collective intelligence; and (2) an assembling strategy for a student-teacher model-based self-training framework to achieve the robust classification of 14 surgical tools in a semi-supervised fashion. Furthermore, we employ strategies such as weighted data loaders and label smoothing to enable the models to learn difficult samples and address class imbalance issues. The proposed methodology achieves an average F1-score of 85.88% for the ensemble model-based self-training with class weights, and 80.88% without class weights for noisy tool labels. Also, our proposed method significantly outperforms existing approaches, which effectively demonstrates its effectiveness.

Item Type:	Article
Identification Number:	10.1038/s41598-024-82351-5
Dates:	Date Event 4 December 2024 Accepted 14 February 2025 Published Online
Subjects:	CAH02 - subjects allied to medicine > CAH02-05 - medical sciences > CAH02-05-03 - biomedical sciences (non-specific) CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions:	Faculty of Business, Law and Social Sciences > Graduate School of Management
Depositing User:	Gemma Tonks
Date Deposited:	07 Mar 2025 15:18
Last Modified:	07 Mar 2025 15:18
URI:	https://www.open-access.bcu.ac.uk/id/eprint/16211

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