Abstract
Designing deep learning (DL) models that adapt to new data without forgetting previously acquired knowledge is important in the medical field where data is generated daily, posing a challenge for model adaptation and knowledge retention. Continual learning (CL) enables models to learn continuously without forgetting, typically on a sequence of domains with known domain identities (e.g. source of data). In this work, we address a more challenging and practical CL scenario where information about the domain identity during training and inference is unavailable. We propose Continual-GEN, a novel forget-free, replay-free, and domain-agnostic subnetwork-based CL method for medical imaging with a focus on skin lesion classification. Continual-GEN proposes an ensemble of groups approach that decomposes the training data for each domain into groups of semantically similar clusters. Given two domains, Continual-GEN assesses the similarity between them based on the distance between their ensembles and assigns a separate subnetwork if the similarity score is low, otherwise updating the same subnetwork to learn both domains. At inference, Continual-GEN selects the best subnetwork using a distance-based metric for each test data, which is directly used for classification. Our quantitative experiments on four skin lesion datasets demonstrate the superiority of Continual-GEN over state-of-the-art CL methods, highlighting its potential for practical applications in medical imaging. Our code: https://github.com/nourhanb/Continual-GEN.
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Bayasi, N., Du, S., Hamarneh, G., Garbi, R. (2023). Continual-GEN: Continual Group Ensembling for Domain-agnostic Skin Lesion Classification. In: Celebi, M.E., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops . MICCAI 2023. Lecture Notes in Computer Science, vol 14393. Springer, Cham. https://doi.org/10.1007/978-3-031-47401-9_1
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