TL;DR
This paper introduces a self-supervised learning method called Magnification Prior Contrastive Similarity (MPCS) that leverages magnification factors to learn effective representations of breast cancer histopathology images without labels, achieving state-of-the-art results with limited labeled data.
Contribution
The paper proposes a novel self-supervised approach that exploits magnification information to improve representation learning in histopathology, reducing reliance on human annotations.
Findings
MPCS matches fully supervised performance with only 20% labeled data.
Outperforms previous supervised methods in fully labeled settings.
Reduces human prior in representation learning for medical images.
Abstract
This work presents a novel self-supervised pre-training method to learn efficient representations without labels on histopathology medical images utilizing magnification factors. Other state-of-theart works mainly focus on fully supervised learning approaches that rely heavily on human annotations. However, the scarcity of labeled and unlabeled data is a long-standing challenge in histopathology. Currently, representation learning without labels remains unexplored for the histopathology domain. The proposed method, Magnification Prior Contrastive Similarity (MPCS), enables self-supervised learning of representations without labels on small-scale breast cancer dataset BreakHis by exploiting magnification factor, inductive transfer, and reducing human prior. The proposed method matches fully supervised learning state-of-the-art performance in malignancy classification when only 20% of…
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Taxonomy
MethodsMagnification Prior Contrastive Similarity
