# CheXFusion: Effective Fusion of Multi-View Features using Transformers for Long-Tailed Chest X-Ray Classification

Source: [CheXFusion: Effective Fusion of Multi-View Features using Transformers for Long-Tailed Chest X-Ray Classification](<https://www.sota2.com/research/paper/68fa70631e209131cea4ace6>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:25:24.889Z

Authors: Dongkyun Kim

Published: 2023-08-08T00:46:01.000Z

## Abstract

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

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Carotid Plaque Grading on Private Carotid US Images 1.0 (test)](<https://www.sota2.com/research/sota/carotid-plaque-grading-on-private-carotid-us-images-1-0-test.md>): Carotid plaque grading; Private Carotid US Images 1.0 (test); Accuracy: 90.58.

- [Binary Classification on CHD dataset (test)](<https://www.sota2.com/research/sota/binary-classification-on-chd-dataset-test.md>): Binary Classification; CHD dataset (test); Accuracy: 91.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2308.03968>)

[PDF](<https://arxiv.org/pdf/2308.03968.pdf>)