{"id":7381,"date":"2025-06-17T18:10:07","date_gmt":"2025-06-17T09:10:07","guid":{"rendered":"https:\/\/www.first.iir.isct.ac.jp\/?p=7381"},"modified":"2025-06-23T11:29:46","modified_gmt":"2025-06-23T02:29:46","slug":"detail_1977","status":"publish","type":"post","link":"https:\/\/www.first.iir.isct.ac.jp\/en\/detail_1977\/","title":{"rendered":"MHP-Net: A revolutionary AI model for accurate liver tumor segmentation for diagnosis and therapy\uff1aProf. Kenji Suzuki\uff08Applied Artificial Intelligence Research Core\uff09"},"content":{"rendered":"<p><strong>Researchers develop a cutting-edge deep-learning model that achieves high accuracy in liver tumor segmentation with a limited training dataset<\/strong><\/p>\n<p><span style=\"font-size: 10pt;\">Signaling a major advance in small-data AI for medical imaging, a research team from Institute of Science Tokyo has developed a novel artificial intelligence model called \u201cMHP-Net,\u201d which delivers cutting-edge performance in liver tumor segmentation with a limited dataset. Unlike conventional models that require thousands of patient cases, this model uses sampling of 3D patches obtained from 7 to 28 tumors. Also, it outperforms the top entry of the MICCAI 2017 (worldwide liver tumor segmentation competition).<\/span><br \/>\n<span style=\"font-size: 10pt;\">MHP-Net: A Smarter AI Tool for Liver Tumor Assessment<\/span><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"\" src=\"https:\/\/www.isct.ac.jp\/cms\/contents\/data\/1\/1797\/component\/file\/2237ee0549412b7e063e6f6298c26556.webp\" width=\"599\" height=\"493\" \/><\/p>\n<h2><span style=\"font-size: 12px;\">Contact<\/span><\/h2>\n<p><span style=\"font-size: 12px;\">Prof. Kenji Suzuki\uff08Applied Artificial Intelligence Research Core\uff09<\/span><br \/>\n<span style=\"font-size: 12px;\"><a href=\"http:\/\/www.bmai.iir.titech.ac.jp\/\" target=\"_blank\" rel=\"noopener\">https:\/\/www.bmai.iir.titech.ac.jp\/<\/a><\/span><br \/>\n<span style=\"font-size: 12px;\"><a style=\"background-color: #ffffff;\" href=\"info@bmai.iir.titech.ac.jp=\u3010\u554f\u5408\u305b\u3011\u30d7\u30ec\u30b9\u30ea\u30ea\u30fc\u30b9\u306b\u3064\u3044\u3066&amp;body=\u3054\u8a18\u5165\u304f\u3060\u3055\u3044\"><img loading=\"lazy\" decoding=\"async\" class=\"mt-image-none\" src=\"https:\/\/www.first.iir.isct.ac.jp\/Contact_en.jpg\" alt=\"https:\/\/www.first.iir.isct.ac.jp\/Contact_en.jpg\" width=\"100\" height=\"30\" \/><\/a><\/span><\/p>\n<div style=\"background: #f3f3f2; padding: 10px; border: none; border-radius: 10px; -moz-border-radius: 10px; -webkit-border-radius: 10px;\"><span style=\"font-size: 12px;\"><strong>Related Site<\/strong><br \/>\n\u25b6Institute of Innovative Research\uff08IIR\uff09<\/span><\/div>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Researchers develop a cutting-edge deep-learning model that achieves high accuracy in liver tumor segmentation with a limited training dataset Signaling a major advance in small-data AI for medical imaging, a research team from Institute of Science Tokyo has developed a novel artificial intelligence model called \u201cMHP-Net,\u201d which delivers cutting-edge performance in liver tumor segmentation with [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_locale":"en_US","_original_post":"https:\/\/www.first.iir.isct.ac.jp\/?p=7353","footnotes":"","_links_to":"","_links_to_target":""},"categories":[17],"tags":[],"class_list":["post-7381","post","type-post","status-publish","format-standard","hentry","category-press-release","en-US"],"_links":{"self":[{"href":"https:\/\/www.first.iir.isct.ac.jp\/wp-json\/wp\/v2\/posts\/7381","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.first.iir.isct.ac.jp\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.first.iir.isct.ac.jp\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.first.iir.isct.ac.jp\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.first.iir.isct.ac.jp\/wp-json\/wp\/v2\/comments?post=7381"}],"version-history":[{"count":6,"href":"https:\/\/www.first.iir.isct.ac.jp\/wp-json\/wp\/v2\/posts\/7381\/revisions"}],"predecessor-version":[{"id":7396,"href":"https:\/\/www.first.iir.isct.ac.jp\/wp-json\/wp\/v2\/posts\/7381\/revisions\/7396"}],"wp:attachment":[{"href":"https:\/\/www.first.iir.isct.ac.jp\/wp-json\/wp\/v2\/media?parent=7381"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.first.iir.isct.ac.jp\/wp-json\/wp\/v2\/categories?post=7381"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.first.iir.isct.ac.jp\/wp-json\/wp\/v2\/tags?post=7381"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}