Learning Underrepresented Classes from Decentralized Partially Labeled Medical Images

• URL: http://arxiv.org/abs/2206.15353v1
• Date: Thu, 30 Jun 2022 15:28:18 GMT
• Title: Learning Underrepresented Classes from Decentralized Partially Labeled Medical Images
• Authors: Nanqing Dong, Michael Kampffmeyer, Irina Voiculescu
• Abstract summary: Using decentralized data for federated training is one promising emerging research direction for alleviating data scarcity in the medical domain. In this paper, we consider a practical yet under-explored problem, where underrepresented classes only have few labeled instances available. We show that standard federated learning approaches fail to learn robust multi-label classifiers with extreme class imbalance.
• Score: 11.500033811355062
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Using decentralized data for federated training is one promising emerging research direction for alleviating data scarcity in the medical domain. However, in contrast to large-scale fully labeled data commonly seen in general object recognition tasks, the local medical datasets are more likely to only have images annotated for a subset of classes of interest due to high annotation costs. In this paper, we consider a practical yet under-explored problem, where underrepresented classes only have few labeled instances available and only exist in a few clients of the federated system. We show that standard federated learning approaches fail to learn robust multi-label classifiers with extreme class imbalance and address it by proposing a novel federated learning framework, FedFew. FedFew consists of three stages, where the first stage leverages federated self-supervised learning to learn class-agnostic representations. In the second stage, the decentralized partially labeled data are exploited to learn an energy-based multi-label classifier for the common classes. Finally, the underrepresented classes are detected based on the energy and a prototype-based nearest-neighbor model is proposed for few-shot matching. We evaluate FedFew on multi-label thoracic disease classification tasks and demonstrate that it outperforms the federated baselines by a large margin.

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