3 research outputs found
Learning Discriminative Representation via Metric Learning for Imbalanced Medical Image Classification
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