Shortcut Learning in Medical Image Segmentation
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866914849653522432 |
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| author | Lin, Manxi Weng, Nina Mikolaj, Kamil Bashir, Zahra Svendsen, Morten Bo Søndergaard Tolsgaard, Martin Christensen, Anders Nymark Feragen, Aasa |
| author_facet | Lin, Manxi Weng, Nina Mikolaj, Kamil Bashir, Zahra Svendsen, Morten Bo Søndergaard Tolsgaard, Martin Christensen, Anders Nymark Feragen, Aasa |
| contents | Shortcut learning is a phenomenon where machine learning models prioritize learning simple, potentially misleading cues from data that do not generalize well beyond the training set. While existing research primarily investigates this in the realm of image classification, this study extends the exploration of shortcut learning into medical image segmentation. We demonstrate that clinical annotations such as calipers, and the combination of zero-padded convolutions and center-cropped training sets in the dataset can inadvertently serve as shortcuts, impacting segmentation accuracy. We identify and evaluate the shortcut learning on two different but common medical image segmentation tasks. In addition, we suggest strategies to mitigate the influence of shortcut learning and improve the generalizability of the segmentation models. By uncovering the presence and implications of shortcuts in medical image segmentation, we provide insights and methodologies for evaluating and overcoming this pervasive challenge and call for attention in the community for shortcuts in segmentation. Our code is public at https://github.com/nina-weng/shortcut_skinseg . |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_06748 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Shortcut Learning in Medical Image Segmentation Lin, Manxi Weng, Nina Mikolaj, Kamil Bashir, Zahra Svendsen, Morten Bo Søndergaard Tolsgaard, Martin Christensen, Anders Nymark Feragen, Aasa Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Shortcut learning is a phenomenon where machine learning models prioritize learning simple, potentially misleading cues from data that do not generalize well beyond the training set. While existing research primarily investigates this in the realm of image classification, this study extends the exploration of shortcut learning into medical image segmentation. We demonstrate that clinical annotations such as calipers, and the combination of zero-padded convolutions and center-cropped training sets in the dataset can inadvertently serve as shortcuts, impacting segmentation accuracy. We identify and evaluate the shortcut learning on two different but common medical image segmentation tasks. In addition, we suggest strategies to mitigate the influence of shortcut learning and improve the generalizability of the segmentation models. By uncovering the presence and implications of shortcuts in medical image segmentation, we provide insights and methodologies for evaluating and overcoming this pervasive challenge and call for attention in the community for shortcuts in segmentation. Our code is public at https://github.com/nina-weng/shortcut_skinseg . |
| title | Shortcut Learning in Medical Image Segmentation |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2403.06748 |