Learning Discriminative Representation via Metric Learning for Imbalanced Medical Image Classification
This work addresses class imbalance in medical image classification, which is an incremental improvement over existing two-stage methods by incorporating metric learning for better fine-grained discrimination.
The paper tackles the problem of biased predictions in medical image classification due to data imbalance between common and rare diseases by embedding metric learning into a two-stage framework to learn more discriminative feature representations, resulting in consistent outperformance over existing one-stage and two-stage approaches on three medical image datasets.
Data imbalance between common and rare diseases during model training often causes intelligent diagnosis systems to have biased predictions towards common diseases. The state-of-the-art approaches apply a two-stage learning framework to alleviate the class-imbalance issue, where the first stage focuses on training of a general feature extractor and the second stage focuses on fine-tuning the classifier head for class rebalancing. However, existing two-stage approaches do not consider the fine-grained property between different diseases, often causing the first stage less effective for medical image classification than for natural image classification tasks. In this study, we propose embedding metric learning into the first stage of the two-stage framework specially to help the feature extractor learn to extract more discriminative feature representations. Extensive experiments mainly on three medical image datasets show that the proposed approach consistently outperforms existing onestage and two-stage approaches, suggesting that metric learning can be used as an effective plug-in component in the two-stage framework for fine-grained class-imbalanced image classification tasks.