Charlton, J. orcid.org/0000-0001-8402-6723, Alsanie, I. and Khurram, S.A. (2024) Whole slide images classification of salivary gland tumours. In: Yap, H.M., Cootes, T., Zwiggelaar, R. and Reeves, N., (eds.) Medical Image Understanding and Analysis (MIUA). 28th UK Conference on Medical Image Understanding and Analysis - MIUA, 24-26 Jul 2024, Manchester, United Kingdom. Frontiers in Medical Technology . Frontiers Media , pp. 159-166. ISBN 978-2-8325-1244-9
Abstract
This work shows promising results using multiple instance learning on salivary gland tumours in classifying cancers on whole slide images. Utilising CTransPath as a patch-level feature extractor and CLAM as a feature aggregator, an F1 score of over 0.88 and AUROC of 0.92 are obtained for detecting cancer in whole slide images.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2024 The authors. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (creativecommons.org/licenses/by/4.0/ ) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. |
Keywords: | medical imaging; Machine Learning/AI; Medical analysis |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Clinical Dentistry (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 23 Aug 2024 10:17 |
Last Modified: | 06 Jan 2025 16:27 |
Status: | Published |
Publisher: | Frontiers Media |
Series Name: | Frontiers in Medical Technology |
Refereed: | Yes |
Identification Number: | 10.3389/978-2-8325-1244-9 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:216395 |