Abstract
Deep learning models for breast cancer detection from mammographic images have significant reliability problems when presented with Out-of-Distribution (OOD) inputs such as other imaging modalities (CT, MRI, X-ray) or equipment variations, leading to unreliable detection and misdiagnosis. The current research mitigates the fundamental OOD issue through a comprehensive approach integrating ResNet50-based OOD filtering with YOLO architectures (YOLOv8, YOLOv11, YOLOv12) for accurate detection of breast cancer. Our strategy establishes an in-domain gallery via cosine similarity to rigidly reject non-mammographic inputs prior to processing, ensuring that only domain-associated images supply the detection pipeline. The OOD detection component achieves 99.77\% general accuracy with immaculate 100\% accuracy on OOD test sets, effectively eliminating irrelevant imaging modalities. ResNet50 was selected as the optimum backbone after 12 CNN architecture searches. The joint framework unites OOD robustness with high detection performance (mAP@0.5: 0.947) and enhanced interpretability through Grad-CAM visualizations. Experimental validation establishes that OOD filtering significantly improves system reliability by preventing false alarms on out-of-distribution inputs while maintaining higher detection accuracy on mammographic data. The present study offers a fundamental foundation for the deployment of reliable AI-based breast cancer detection systems in diverse clinical environments with inherent data heterogeneity.
Abstract (translated)
用于从乳腺X线摄影图像中检测乳腺癌的深度学习模型在面对与训练数据不同的“Out-of-Distribution”（OOD，非预期分布）输入时存在显著可靠性问题。这类OOD输入包括其他成像模式（如CT、MRI、X射线）或设备变化等，这些问题会导致检测不可靠和误诊。目前的研究通过一种综合性方法解决了这一根本的OOD问题，该方法结合了基于ResNet50的OOD过滤器与YOLO架构（包括YOLOv8、YOLOv11和YOLOv12），以实现乳腺癌的精确检测。 我们的策略通过余弦相似度建立了域内图像库，并严格拒绝非乳腺X线摄影输入，确保只有相关领域的图像才能进入检测流程。OOD检测组件实现了99.77%的一般准确率，在OOD测试集上达到了完美的100%准确率，有效地排除了其他成像模式的干扰。 经过12次CNN架构搜索后，选择了ResNet50作为最优骨干网络。该联合框架结合了OOD鲁棒性、高检测性能（mAP@0.5：0.947）以及通过Grad-CAM可视化增强的可解释性。实验验证表明，OOD过滤显著提高了系统的可靠性，减少了对非预期输入产生误报的情况，同时保持了乳腺X线摄影数据上的高检测准确性。 本研究为在存在数据异质性的多样临床环境中部署可靠的人工智能乳腺癌检测系统提供了基础框架。
URL
https://arxiv.org/abs/2512.00129
