Classification of Luminal Subtypes in Full Mammogram Images Using Transfer Learning

TL;DR

This study explores using transfer learning on full mammogram images with only image-level labels to classify luminal subtypes, aiming to assist clinical decision-making in breast cancer treatment.

Contribution

It introduces a transfer learning approach with ResNet-18 for luminal subtype classification using only image-level labels, reducing dependency on detailed annotations.

Findings

Achieved a mean AUC of 0.6688

Achieved a mean F1 score of 0.6693

Significant improvement over baseline (p<0.0001)

Abstract

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.