用多视角聚合提升胸部X光片长尾分类效果
Ensemble of ConvNeXt V2 and MaxViT for Long-Tailed CXR Classification with View-Based Aggregation
- 融合ConvNeXt V2与MaxViT模型，结合视图级预测融合
- 在长尾分布下实现4/5名的挑战赛成绩，提升检测准确率
- 适合医疗影像长尾问题研究者参考
本文针对MICCAI 2024 CXR-LT挑战赛提出解决方案，在子任务1中获第5名，子任务2中获第4名。通过集成在外部胸片数据集上预训练的ConvNeXt V2与MaxViT模型，应对胸片病灶的长尾分布问题。方法结合当前最先进的图像分类技术、非对称损失处理类别不平衡，并采用视图级预测聚合策略提升分类性能。实验表明，该方法有效提升了检测准确率并改善了长尾分布下的表现。代码已开源：https://github.com/yamagishi0824/cxrlt24-multiview-pp。
原文摘要 · Abstract (English)
In this work, we present our solution for the MICCAI 2024 CXR-LT challenge, achieving 4th place in Subtask 2 and 5th in Subtask 1. We leveraged an ensemble of ConvNeXt V2 and MaxViT models, pretrained on an external chest X-ray dataset, to address the long-tailed distribution of chest findings. The proposed method combines state-of-the-art image classification techniques, asymmetric loss for handling class imbalance, and view-based prediction aggregation to enhance classification performance. Through experiments, we demonstrate the advantages of our approach in improving both detection accuracy and the handling of the long-tailed distribution in CXR findings. The code is available at https://github.com/yamagishi0824/cxrlt24-multiview-pp.
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