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Publication
Mammogram retrieval system: Aggregating image classifiers for enhanced breast cancer diagnosis
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Advisor(s)
Abstract(s)
Breast cancer remains a significant global health concern. This study presents an image retrieval system to aid specialists in the analysis of mammogram images. The system employs individual classifiers for eight dimensions: laterality, view, breast density, BI-RADS classification, masses, calcifications, distortions, and asymmetries. Four pre-trained networks, ResNet50, VGG16, InceptionV3, and InceptionResNetV2, were used to train these classifiers. The retrieval model combines these classifiers through a weighted sum. Four weight assignment strategies were explored, ranging from equal weights to weights based on empirical, literature-based, and specialist-informed considerations. Results are illustrated using the INBreast database, which comprises 410 images. Besides the native annotations, ground truth to validate retrieval models had to be acquired. Classification accuracy is as high as 100% for some of the dimensions. Results also demonstrate the effectiveness of the proposed weighted-sum approach, with variations in weight assignments impacting model performance.
Description
Keywords
Mammogram retrieval system Breast cancer diagnosis Image classification Medical imaging Deep learning
Pedagogical Context
Citation
Roriz, C., Moreira, I., Vasconcelos, V., & Domingues, I. (2024). Mammogram Retrieval System: Aggregating Image Classifiers for Enhanced Breast Cancer Diagnosis. IMIP '24: Proceedings of the 2024 6th International Conference on Intelligent Medicine and Image Processing, 1–8. https://doi.org/10.1145/3669828.3669829
Publisher
ACM - Association for Computing Machinery