Mitigating Catastrophic Forgetting in the Incremental Learning of Medical Images
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908340854980608 |
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| author | Yavari, Sara Furst, Jacob |
| author_facet | Yavari, Sara Furst, Jacob |
| contents | This paper proposes an Incremental Learning (IL) approach to enhance the accuracy and efficiency of deep learning models in analyzing T2-weighted (T2w) MRI medical images prostate cancer detection using the PI-CAI dataset. We used multiple health centers' artificial intelligence and radiology data, focused on different tasks that looked at prostate cancer detection using MRI (PI-CAI). We utilized Knowledge Distillation (KD), as it employs generated images from past tasks to guide the training of models for subsequent tasks. The approach yielded improved performance and faster convergence of the models. To demonstrate the versatility and robustness of our approach, we evaluated it on the PI-CAI dataset, a diverse set of medical imaging modalities including OCT and PathMNIST, and the benchmark continual learning dataset CIFAR-10. Our results indicate that KD can be a promising technique for IL in medical image analysis in which data is sourced from individual health centers and the storage of large datasets is not feasible. By using generated images from prior tasks, our method enables the model to retain and apply previously acquired knowledge without direct access to the original data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_20033 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Mitigating Catastrophic Forgetting in the Incremental Learning of Medical Images Yavari, Sara Furst, Jacob Computer Vision and Pattern Recognition I.2.6; I.2.10 This paper proposes an Incremental Learning (IL) approach to enhance the accuracy and efficiency of deep learning models in analyzing T2-weighted (T2w) MRI medical images prostate cancer detection using the PI-CAI dataset. We used multiple health centers' artificial intelligence and radiology data, focused on different tasks that looked at prostate cancer detection using MRI (PI-CAI). We utilized Knowledge Distillation (KD), as it employs generated images from past tasks to guide the training of models for subsequent tasks. The approach yielded improved performance and faster convergence of the models. To demonstrate the versatility and robustness of our approach, we evaluated it on the PI-CAI dataset, a diverse set of medical imaging modalities including OCT and PathMNIST, and the benchmark continual learning dataset CIFAR-10. Our results indicate that KD can be a promising technique for IL in medical image analysis in which data is sourced from individual health centers and the storage of large datasets is not feasible. By using generated images from prior tasks, our method enables the model to retain and apply previously acquired knowledge without direct access to the original data. |
| title | Mitigating Catastrophic Forgetting in the Incremental Learning of Medical Images |
| topic | Computer Vision and Pattern Recognition I.2.6; I.2.10 |
| url | https://arxiv.org/abs/2504.20033 |