Classification of Luminal Subtypes in Full Mammogram Images Using Transfer Learning
This work addresses breast cancer therapy planning by automating subtype identification, but it is incremental as it applies transfer learning to a known bottleneck.
The paper tackled luminal subtype classification in full mammogram images using only image-level labels, achieving a mean AUC of 0.6688 and F1 score of 0.6693, significantly outperforming the baseline.
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.