Mitigating Catastrophic Forgetting in the Incremental Learning of Medical Images
This addresses the problem of data storage limitations and forgetting in medical AI for healthcare centers, but it is incremental as it applies an existing technique to a specific domain.
The paper tackled catastrophic forgetting in incremental learning for medical image analysis, specifically prostate cancer detection on T2-weighted MRI using the PI-CAI dataset, and found that Knowledge Distillation improved performance and convergence speed.
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.