CheXFusion: Effective Fusion of Multi-View Features using Transformers
  for Long-Tailed Chest X-Ray Classification
Dongkyun Kim
Medical image classification poses unique challenges due to the long-tailed
distribution of diseases, the co-occurrence of diagnostic findings, and the
multiple views available for each study or patient. This paper introduces our
solution to the ICCV CVAMD 2023 Shared Task on CXR-LT: Multi-Label Long-Tailed
Classification on Chest X-Rays. Our approach introduces CheXFusion, a
transformer-based fusion module incorporating multi-view images. The fusion
module, guided by self-attention and cross-attention mechanisms, efficiently
aggregates multi-view features while considering label co-occurrence.
Furthermore, we explore data balancing and self-training methods to optimize
the model's performance. Our solution achieves state-of-the-art results with
0.372 mAP in the MIMIC-CXR test set, securing 1st place in the competition. Our
success in the task underscores the significance of considering multi-view
settings, class imbalance, and label co-occurrence in medical image
classification. Public code is available at
https://github.com/dongkyuk/CXR-LT-public-solution