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

• URL: http://arxiv.org/abs/2410.12006v1
• Date: Tue, 15 Oct 2024 19:13:05 GMT
• Title: Beyond Labels: A Self-Supervised Framework with Masked Autoencoders and Random Cropping for Breast Cancer Subtype Classification
• Authors: Annalisa Chiocchetti, Marco Dossena, Christopher Irwin, Luigi Portinale,
• Abstract summary: We learn a self-supervised embedding tailored for computer vision tasks in this domain. We generate a large dataset from WSIs automatically. We evaluate our model's performance on the BRACS dataset and compare it with existing benchmarks.
• Score: 0.3374875022248865
• License:
• Abstract: This work contributes to breast cancer sub-type classification using histopathological images. We utilize masked autoencoders (MAEs) to learn a self-supervised embedding tailored for computer vision tasks in this domain. This embedding captures 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 WSIs automatically. Additionally, we assess the performance of linear probes for multi-class classification tasks of cancer sub-types using the representations learnt 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 dataset and compare it with existing benchmarks.

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