{"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/supervision-and-source-domain-impact-on","title":"Supervision and Source Domain Impact on Representation Learning: A Histopathology Case Study","arxiv_id":"2005.08629","date":"2020-05-10","proceeding":null,"authors":["Milad Sikaroudi","Amir Safarpoor","Benyamin Ghojogh","Sobhan Shafiei","Mark Crowley","H. R. Tizhoosh"],"abstract":"As many algorithms depend on a suitable representation of data, learning unique features is considered a crucial task. Although supervised techniques using deep neural networks have boosted the performance of representation learning, the need for a large set of labeled data limits the application of such methods. As an example, high-quality delineations of regions of interest in the field of pathology is a tedious and time-consuming task due to the large image dimensions. In this work, we explored the performance of a deep neural network and triplet loss in the area of representation learning. We investigated the notion of similarity and dissimilarity in pathology whole-slide images and compared different setups from unsupervised and semi-supervised to supervised learning in our experiments. Additionally, different approaches were tested, applying few-shot learning on two publicly available pathology image datasets. We achieved high accuracy and generalization when the learned representations were applied to two different pathology datasets.","url_abs":"https://arxiv.org/abs/2005.08629v1","url_pdf":"https://arxiv.org/pdf/2005.08629v1.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":"supervision-and-source-domain-impact-on","repo_url":"https://github.com/bghojogh/Siamese-Network-Histopathology","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"dimensionality-reduction","task_name":"Dimensionality Reduction"},{"task_slug":"domain-generalization","task_name":"Domain Generalization"},{"task_slug":"few-shot-learning","task_name":"Few-Shot Learning"},{"task_slug":"histopathological-image-classification","task_name":"Histopathological Image Classification"},{"task_slug":"metric-learning","task_name":"Metric Learning"},{"task_slug":"representation-learning","task_name":"Representation Learning"},{"task_slug":null,"task_name":"Triplet"},{"task_slug":"whole-slide-images","task_name":"whole slide images"}],"methods":[{"method_slug":"triplet-loss","method_name":"Triplet Loss"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}