{"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/capsule-networks-against-medical-imaging-data","title":"Capsule Networks against Medical Imaging Data Challenges","arxiv_id":"1807.07559","date":"2018-07-19","proceeding":null,"authors":["Amelia Jiménez-Sánchez","Shadi Albarqouni","Diana Mateus"],"abstract":"A key component to the success of deep learning is the availability of\nmassive amounts of training data. Building and annotating large datasets for\nsolving medical image classification problems is today a bottleneck for many\napplications. Recently, capsule networks were proposed to deal with\nshortcomings of Convolutional Neural Networks (ConvNets). In this work, we\ncompare the behavior of capsule networks against ConvNets under typical\ndatasets constraints of medical image analysis, namely, small amounts of\nannotated data and class-imbalance. We evaluate our experiments on MNIST,\nFashion-MNIST and medical (histological and retina images) publicly available\ndatasets. Our results suggest that capsule networks can be trained with less\namount of data for the same or better performance and are more robust to an\nimbalanced class distribution, which makes our approach very promising for the\nmedical imaging community.","url_abs":"http://arxiv.org/abs/1807.07559v1","url_pdf":"http://arxiv.org/pdf/1807.07559v1.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":"capsule-networks-against-medical-imaging-data","repo_url":"https://github.com/ameliajimenez/capsule-networks-medical-data-challenges","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"medical-image-analysis","task_name":"Medical Image Analysis"},{"task_slug":"medical-image-classification","task_name":"Medical Image Classification"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}