{"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/graphxnet-chest-x-ray-classification-under","title":"GraphX$^{NET}-$ Chest X-Ray Classification Under Extreme Minimal Supervision","arxiv_id":"1907.10085","date":"2019-07-23","proceeding":null,"authors":["Angelica I. Aviles-Rivero","Nicolas Papadakis","Ruoteng Li","Philip Sellars","Qingnan Fan","Robby T. Tan","Carola-Bibiane Schönlieb"],"abstract":"The task of classifying X-ray data is a problem of both theoretical and clinical interest. Whilst supervised deep learning methods rely upon huge amounts of labelled data, the critical problem of achieving a good classification accuracy when an extremely small amount of labelled data is available has yet to be tackled. In this work, we introduce a novel semi-supervised framework for X-ray classification which is based on a graph-based optimisation model. To the best of our knowledge, this is the first method that exploits graph-based semi-supervised learning for X-ray data classification. Furthermore, we introduce a new multi-class classification functional with carefully selected class priors which allows for a smooth solution that strengthens the synergy between the limited number of labels and the huge amount of unlabelled data. We demonstrate, through a set of numerical and visual experiments, that our method produces highly competitive results on the ChestX-ray14 data set whilst drastically reducing the need for annotated data.","url_abs":"https://arxiv.org/abs/1907.10085v3","url_pdf":"https://arxiv.org/pdf/1907.10085v3.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":[],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"multi-class-classification","task_name":"Multi-class Classification"},{"task_slug":"semi-supervised-medical-image-classification","task_name":"Semi-supervised Medical Image Classification"},{"task_slug":"x-ray-classification","task_name":"X-ray Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/semi-supervised-medical-image-classification-1","task":"Semi-supervised Medical Image Classification","dataset":"Chest X-Ray14 2% labeled","model":"Graph XNet","rank_in_archive_order":4,"of":4,"metrics":{"AUC":"53"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1907.10085","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}