Papers › Magnification Prior: A Self-Supervised Method for Learning Representations on Breast...
Magnification Prior: A Self-Supervised Method for Learning Representations on Breast Cancer Histopathological Images
Prakash Chandra Chhipa, Richa Upadhyay, Gustav Grund Pihlgren, Rajkumar Saini, Seiichi Uchida, Marcus Liwicki
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
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Breast Cancer Histology Image Classification | BreakHis | EfficientNet-b2 | 1:1 Accuracy | 92.23 | #5 of 5 | Archive leaderboard | report |
| Breast Cancer Histology Image Classification | BreakHis | EfficientNet-b2 | Accuracy (Inter-Patient) | 92.15 | #5 of 5 | Archive leaderboard | report |
| Breast Cancer Histology Image Classification (20% labels) | BreakHis | EfficientNet-b2 | 1:1 Accuracy | 88.77 | #1 of 1 | Archive leaderboard | report |
| Breast Cancer Histology Image Classification (20% labels) | BreakHis | EfficientNet-b2 | Accuracy (Inter-Patient) | 88.77 | #1 of 1 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Introduced by this paper: Magnification Prior Contrastive Similarity
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