Beyond Labels: A Self-Supervised Framework with Masked Autoencoders and Random Cropping for Breast Cancer Subtype Classification

This work addresses the problem of breast cancer sub-type classification using histopathological image analysis. We utilize masked autoencoders (MAEs) based on Visual Transformer (ViT) to learn, through Self-Supervised Learning, embeddings tailored to computer vision tasks in this domain. Such embeddings capture informative representations of histopathological data, facilitating feature learning without extensive labeled datasets. During pre-training, we investigate employing a random crop technique to generate a large dataset from whole-slide images automatically. Additionally, we assess the performance of linear probes for multi-class classification tasks of cancer sub-types using the representations learned by the MAE. Our approach aims to achieve strong performance on downstream tasks by leveraging the complementary strengths of ViTs and autoencoders. We evaluate our model's performance on the BRACS and BACH datasets and compare it with existing benchmarks.