  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
