Teevno, M.A., Ochoa-Ruiz, G. and Ali, S. orcid.org/0000-0003-1313-3542 (2023) A semi-supervised Teacher-Student framework for surgical tool detection and localization. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 11 (4). 1033 -1041. ISSN 2168-1163
Abstract
Surgical tool detection in minimally invasive surgery is an essential part of computer-assisted interventions. Current approaches are mostly based on supervised methods requiring large annotated datasets. However, labelled datasets are often scarce. Semi-supervised learning (SSL) has recently emerged as a viable alternative showing promise in producing models retaining competitive performance to supervised methods. Therefore, this paper introduces an SSL framework in the surgical tool detection paradigm, which aims to mitigate training data scarcity and data imbalance problems through a knowledge distillation approach. In the proposed work, we train a model with labelled data which initialises the Teacher-Student joint learning, where the Student is trained on Teacher-generated pseudo-labels from unlabelled data. We also propose a multi-class distance with a margin-based classification loss function in the region-of-interest head of the detector to segregate the foreground-background region effectively. Our results on m2cai16-tool-locations dataset indicates the superiority of our approach on different supervised data settings (1%, 2%, 5% and 10% of annotated data) where our model achieves overall improvements of 8%, 12%, and 27% in mean average precision on 1% labelled data over the state-of-the-art SSL methods and the supervised baseline, respectively. The code is available at https://github.com/Mansoor-at/Semi-supervised-surgical-tool-detection.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
Keywords: | Semi-supervised learning; Faster-RCNN; surgical tool detection |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 08 Dec 2022 10:55 |
Last Modified: | 15 Jan 2024 13:06 |
Status: | Published |
Publisher: | Taylor & Francis |
Identification Number: | 10.1080/21681163.2022.2150688 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:194155 |