{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/magnification-prior-a-self-supervised-method","title":"Magnification Prior: A Self-Supervised Method for Learning Representations on Breast Cancer Histopathological Images","arxiv_id":"2203.07707","date":"2022-03-15","proceeding":null,"authors":["Prakash Chandra Chhipa","Richa Upadhyay","Gustav Grund Pihlgren","Rajkumar Saini","Seiichi Uchida","Marcus Liwicki"],"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","url_abs":"https://arxiv.org/abs/2203.07707v2","url_pdf":"https://arxiv.org/pdf/2203.07707v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"magnification-prior-a-self-supervised-method","repo_url":"https://github.com/prakashchhipa/magnification-prior-self-supervised-method","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"breast-cancer-histology-image-classification","task_name":"Breast Cancer Histology Image Classification"},{"task_slug":"breast-cancer-histology-image-classification-1","task_name":"Breast Cancer Histology Image Classification (20% labels)"},{"task_slug":"classification-of-breast-cancer-histology","task_name":"Classification Of Breast Cancer Histology Images"},{"task_slug":"representation-learning","task_name":"Representation Learning"},{"task_slug":"self-supervised-learning","task_name":"Self-Supervised Learning"}],"methods":[{"method_slug":"magnification-prior-contrastive-similarity","method_name":"Magnification Prior Contrastive Similarity"}],"datasets_introduced":[],"methods_introduced":[{"slug":"magnification-prior-contrastive-similarity","name":"Magnification Prior Contrastive Similarity","full_name":"Magnification Prior Contrastive Similarity"}],"results":[{"leaderboard":"/sota/breast-cancer-histology-image-classification","task":"Breast Cancer Histology Image Classification","dataset":"BreakHis","model":"EfficientNet-b2","rank_in_archive_order":5,"of":5,"metrics":{"1:1 Accuracy":"92.23","Accuracy (Inter-Patient)":"92.15"},"uses_additional_data":false},{"leaderboard":"/sota/breast-cancer-histology-image-classification-1","task":"Breast Cancer Histology Image Classification (20% labels)","dataset":"BreakHis","model":"EfficientNet-b2","rank_in_archive_order":1,"of":1,"metrics":{"1:1 Accuracy":"88.77","Accuracy (Inter-Patient)":"88.77"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}