Analysis of Incursive Breast Cancer in Mammograms Using YOLO, Explainability, and Domain Adaptation

• URL: http://arxiv.org/abs/2512.00129v1
• Date: Fri, 28 Nov 2025 08:48:05 GMT
• Title: Analysis of Incursive Breast Cancer in Mammograms Using YOLO, Explainability, and Domain Adaptation
• Authors: Jayan Adhikari, Prativa Joshi, Susish Baral,
• Abstract summary: Deep learning models for breast cancer detection from mammographic images have significant reliability problems when presented with Out-of-Distribution inputs.<n>We develop a comprehensive approach integrating ResNet50-based OOD filtering with YOLO architectures for accurate detection of breast cancer.<n>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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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