{"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/towards-deep-cellular-phenotyping-in","title":"Towards Deep Cellular Phenotyping in Placental Histology","arxiv_id":"1804.03270","date":"2018-04-09","proceeding":null,"authors":["Michael Ferlaino","Craig A. Glastonbury","Carolina Motta-Mejia","Manu Vatish","Ingrid Granne","Stephen Kennedy","Cecilia M. Lindgren","Christoffer Nellåker"],"abstract":"The placenta is a complex organ, playing multiple roles during fetal\ndevelopment. Very little is known about the association between placental\nmorphological abnormalities and fetal physiology. In this work, we present an\nopen sourced, computationally tractable deep learning pipeline to analyse\nplacenta histology at the level of the cell. By utilising two deep\nConvolutional Neural Network architectures and transfer learning, we can\nrobustly localise and classify placental cells within five classes with an\naccuracy of 89%. Furthermore, we learn deep embeddings encoding phenotypic\nknowledge that is capable of both stratifying five distinct cell populations\nand learn intraclass phenotypic variance. We envisage that the automation of\nthis pipeline to population scale studies of placenta histology has the\npotential to improve our understanding of basic cellular placental biology and\nits variations, particularly its role in predicting adverse birth outcomes.","url_abs":"http://arxiv.org/abs/1804.03270v2","url_pdf":"http://arxiv.org/pdf/1804.03270v2.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":"towards-deep-cellular-phenotyping-in","repo_url":"https://github.com/Nellaker-group/TowardsDeepPhenotyping","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}