解决医学影像分类中罕见病样本少导致的诊断偏差问题。
Long-Tailed Medical Image Classification
- 采用数据增强等深度学习技术缓解长尾分布影响
- 最佳模型在验证集上多指标表现显著优于基线
- 适合医疗AI研发与临床辅助诊断系统应用
本文探讨了因长尾分布（罕见疾病样本极少）导致标准医学图像分类方法失效的问题，表现为对常见病过度诊断而忽略罕见病。为此，我们引入数据增强等深度学习技术以降低错误率，尤其针对罕见疾病。在验证集上通过平均精度（AP）、F1分数、AUROC和损失值评估多种模型性能。结果表明，最优模型在各项指标上均取得显著提升，展现出在医疗领域实际应用的潜力。
原文摘要 · Abstract (English)
In this paper, we examine the difficulties of using standard techniques for medical image classification due to long-tailed distributions (wherein rarer conditions have very few samples) resulting in bias towards diagnosing common diseases and away from rarer diseases. We then discuss and implement deep learning models with techniques such as augmentation to minimize error, especially from rarer diseases. We evaluate various different models with AP, F1 score, AUROC, and loss (all on the validation set). We conclude with the promising results from our best model, and potential applications in the healthcare space.
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