Abstract
Multi-centre colonoscopy images from various medical centres exhibit distinct complicating factors and overlays that impact the image content, contingent on the specific acquisition centre. Existing Deep Segmentation networks struggle to achieve adequate generalizability in such data sets, and the currently available data augmentation methods do not effectively address these sources of data variability. As a solution, we introduce an innovative data augmentation approach centred on interpretability saliency maps, aimed at enhancing the generalizability of Deep Learning models within the realm of multi-centre colonoscopy image segmentation. The proposed augmentation technique demonstrates increased robustness across different segmentation models and domains. Thorough testing on a publicly available multi-centre dataset for polyp detection demonstrates the effectiveness and versatility of our approach, which is observed both in quantitative and qualitative results. The code is publicly available at: https://github.com/nki-radiology/interpretability_augmentation.
Original language | English |
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Title of host publication | Machine Learning in Medical Imaging - 14th International Workshop, MLMI 2023, Held in Conjunction with MICCAI 2023, Proceedings |
Editors | Xiaohuan Cao, Xi Ouyang, Xuanang Xu, Islem Rekik, Zhiming Cui |
Publisher | Springer Verlag |
Pages | 330-340 |
Number of pages | 11 |
Volume | 14348 LNCS |
ISBN (Print) | 9783031456725 |
DOIs | |
Publication status | Published - 1 Jan 2024 |
Event | 14th International Workshop on Machine Learning in Medical Imaging, MLMI 2023 - Vancouver, Canada Duration: 8 Oct 2023 → 8 Oct 2023 https://sites.google.com/view/mlmi2023 |
Publication series
Series | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 14348 LNCS |
ISSN | 0302-9743 |
Workshop
Workshop | 14th International Workshop on Machine Learning in Medical Imaging, MLMI 2023 |
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Abbreviated title | MLMI 2023 |
Country/Territory | Canada |
City | Vancouver |
Period | 8/10/23 → 8/10/23 |
Internet address |