Magnification Prior: A Self-Supervised Method for Learning Representations on Breast Cancer Histopathological Images
This addresses the challenge of data scarcity in histopathology by enabling label-free representation learning, which is incremental as it builds on self-supervised techniques but applies them to a new domain.
The paper tackles the problem of learning representations without labels on breast cancer histopathological images by proposing a self-supervised method that exploits magnification factors, achieving state-of-the-art performance in malignancy classification with only 20% of labels used in fine-tuning.
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 labels are used in fine-tuning and outperform previous works in fully supervised learning settings. It formulates a hypothesis and provides empirical evidence to support that reducing human-prior leads to efficient representation learning in self-supervision. The implementation of this work is available online on GitHub - https://github.com/prakashchhipa/Magnification-Prior-Self-Supervised-Method