Bhandary Panambur A, Madhu P, Maier A (2025)
Publication Status: Submitted
Publication Type: Unpublished / Preprint
Future Publication Type: Journal article
Publication year: 2025
DOI: 10.48550/arXiv.2301.09282
Automatic identification of patients with luminal and non-luminal subtypes during a routine mammography screening can support clinicians in streamlining breast cancer therapy planning. Recent machine learning techniques have shown promising results in molecular subtype classification in mammography; however, they are highly dependent on pixel-level annotations, handcrafted, and radiomic features. In this work, we provide initial insights into the luminal subtype classification in full mammogram images trained using only image-level labels. Transfer learning is applied from a breast abnormality classification task, to finetune a ResNet-18-based luminal versus non-luminal subtype classification task. We present and compare our results on the publicly available CMMD dataset and show that our approach significantly outperforms the baseline classifier by achieving a mean AUC score of 0.6688 and a mean F1 score of 0.6693 on the test dataset. The improvement over baseline is statistically significant, with a p-value of p<0.0001.
APA:
Bhandary Panambur, A., Madhu, P., & Maier, A. (2025). Classification of Luminal Subtypes in Full Mammogram Images Using Transfer Learning. (Unpublished, Submitted).
MLA:
Bhandary Panambur, Adarsh, Prathmesh Madhu, and Andreas Maier. Classification of Luminal Subtypes in Full Mammogram Images Using Transfer Learning. Unpublished, Submitted. 2025.
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