用少量MRI影像实现可解释的前列腺癌自动检测，效果优于人类放射科医生。
Interpretable Prostate Cancer Detection using a Small Cohort of MRI Images
- 基于迁移学习与数据增强，在162例小样本上训练可解释模型。
- ResNet18达90.9%准确率、95.2%敏感性，优于视觉变压器。
- 适合资源有限但需高精度筛查的临床场景，代码数据公开。
前列腺癌是男性主要死因之一，但T2加权MRI的解读因病灶细微且异质性强而困难。我们利用仅162张T2加权图像（102例癌症，60例正常）的小数据集，开发了一种可解释的自动检测框架，通过迁移学习与数据增强缓解数据稀缺问题。系统对比了视觉变换器（ViT、Swin）、CNN（ResNet18）及传统方法（逻辑回归、SVM、HOG+SVM）。转移学习的ResNet18表现最佳（准确率90.9%，敏感性95.2%，AUC 0.905），参数仅1100万；尽管视觉变换器复杂度更高，性能却更低。值得注意的是，HOG+SVM达到相近准确率（AUC 0.917），表明手工特征在小样本中仍有效。本方法仅依赖T2加权图像，无需双参数MRI（T2+DWI）和大规模队列，降低采集与计算成本。22例的阅片者研究显示，五名放射科医生平均敏感性为67.5%（Fleiss Kappa = 0.524），远低于AI模型的95.2%，提示该AI模型可用于辅助筛查，减少漏诊并提升一致性。代码与数据已公开。
原文摘要 · Abstract (English)
Prostate cancer is a leading cause of mortality in men, yet interpretation of T2-weighted prostate MRI remains challenging due to subtle and heterogeneous lesions. We developed an interpretable framework for automatic cancer detection using a small dataset of 162 T2-weighted images (102 cancer, 60 normal), addressing data scarcity through transfer learning and augmentation. We performed a comprehensive comparison of Vision Transformers (ViT, Swin), CNNs (ResNet18), and classical methods (Logistic Regression, SVM, HOG+SVM). Transfer-learned ResNet18 achieved the best performance (90.9% accuracy, 95.2% sensitivity, AUC 0.905) with only 11M parameters, while Vision Transformers showed lower performance despite substantially higher complexity. Notably, HOG+SVM achieved comparable accuracy (AUC 0.917), highlighting the effectiveness of handcrafted features in small datasets. Unlike state-of-the-art approaches relying on biparametric MRI (T2+DWI) and large cohorts, our method achieves competitive performance using only T2-weighted images, reducing acquisition complexity and computational cost. In a reader study of 22 cases, five radiologists achieved a mean sensitivity of 67.5% (Fleiss Kappa = 0.524), compared to 95.2% for the AI model, suggesting potential for AI-assisted screening to reduce missed cancers and improve consistency. Code and data are publicly available.
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