# Magnification Prior: A Self-Supervised Method for Learning Representations on Breast Cancer Histopathological Images Source: [Magnification Prior: A Self-Supervised Method for Learning Representations on Breast Cancer Histopathological Images]() Collection index: [llms.txt]() Data updated: 2026-01-02T13:11:45.765Z Authors: Prakash Chandra Chhipa, Richa Upadhyay, Gustav Grund Pihlgren, Rajkumar Saini, Seiichi Uchida, Marcus Liwicki Published: 2022-03-15T07:54:20.000Z ## 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 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 ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Breast Cancer Cell Classification on Breast Cancer Cell Dataset (test)](): Breast Cancer Cell Classification; Breast Cancer Cell Dataset (test); Accuracy: 98.18. - [Four-class classification on BACH Image-wise (test)](): Four-class classification; BACH Image-wise (test); Accuracy: 91.85. - [Four-class classification on BACH Patch-wise (test)](): Four-class classification; BACH Patch-wise (test); Accuracy: 83.13. - [Four-class classification on BACH Image-wise (val)](): Four-class classification; BACH Image-wise (val); Accuracy: 93.31. - [Four-class classification on BACH Patch-wise (val)](): Four-class classification; BACH Patch-wise (val); Accuracy: 84.25. ## Original sources and implementation [Original paper]() [PDF]() [Implementation]()