{"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/capsdemm-capsule-network-for-detection-of","title":"CapsDeMM: Capsule network for Detection of Munro's Microabscess in skin biopsy images","arxiv_id":"1808.06428","date":"2018-08-20","proceeding":null,"authors":["Anabik Pal","Akshay Chaturvedi","Utpal Garain","Aditi Chandra","Raghunath Chatterjee","Swapan Senapati"],"abstract":"This paper presents an approach for automatic detection of Munro's\nMicroabscess in stratum corneum (SC) of human skin biopsy in order to realize a\nmachine assisted diagnosis of Psoriasis. The challenge of detecting neutrophils\nin presence of nucleated cells is solved using the recent advances of deep\nlearning algorithms. Separation of SC layer, extraction of patches from the\nlayer followed by classification of patches with respect to presence or absence\nof neutrophils form the basis of the overall approach which is effected through\nan integration of a U-Net based segmentation network and a capsule network for\nclassification. The novel design of the present capsule net leads to a drastic\nreduction in the number of parameters without any noticeable compromise in the\noverall performance. The research further addresses the challenge of dealing\nwith Mega-pixel images (in 10X) vis-a-vis Giga-pixel ones (in 40X). The\npromising result coming out of an experiment on a dataset consisting of 273\nreal-life images shows that a practical system is possible based on the present\nresearch. The implementation of our system is available at\nhttps://github.com/Anabik/CapsDeMM.","url_abs":"http://arxiv.org/abs/1808.06428v2","url_pdf":"http://arxiv.org/pdf/1808.06428v2.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":"capsdemm-capsule-network-for-detection-of","repo_url":"https://github.com/Anabik/CapsDeMM","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"}],"methods":[{"method_slug":"fixcaps","method_name":"Capsule Network"},{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"u-net","method_name":"U-Net"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}