Magnification Prior: A Self-Supervised Method for Learning Representations on Breast Cancer Histopathological Images
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
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Datasets
BreakHis
Results from the Paper
Ranked #1 on
Breast Cancer Histology Image Classification
on BreakHis
(Accuracy (Inter-Patient) metric)
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
|---|---|---|---|---|---|---|
| Breast Cancer Histology Image Classification | BreakHis | EfficientNet-b2 | Accuracy (Inter-Patient) | 92.15 | # 1 | |
| 1:1 Accuracy | 92.23 | # 1 | ||||
| Breast Cancer Histology Image Classification (20% labels) | BreakHis | EfficientNet-b2 | 1:1 Accuracy | 88.77 | # 1 | |
| Accuracy (Inter-Patient) | 88.77 | # 1 |
