{"id":62629,"date":"2024-02-26T15:46:43","date_gmt":"2024-02-26T13:46:43","guid":{"rendered":"https:\/\/www.iemn.fr\/?p=62629"},"modified":"2024-02-26T15:47:15","modified_gmt":"2024-02-26T13:47:15","slug":"these-m-afnouch-machine-learning-applications-in-medical-diagnosis-case-study-bone-metastasis","status":"publish","type":"post","link":"https:\/\/www.iemn.fr\/en\/these-2023\/these-m-afnouch-machine-learning-applications-in-medical-diagnosis-case-study-bone-metastasis.html","title":{"rendered":"THESIS: M. AFNOUCH: Machine Learning Applications in Medical Diagnosis, case study: bone metastasis"},"content":{"rendered":"<div id='layer_slider_1'  class='avia-layerslider main_color avia-shadow  avia-builder-el-0  el_before_av_heading  avia-builder-el-first  container_wrap sidebar_right'  style='height: 261px;'  ><div id=\"layerslider_58_s7l8t2yg1gm7\" data-ls-slug=\"homepageslider\" class=\"ls-wp-container fitvidsignore ls-selectable\" style=\"width:1140px;height:260px;margin:0 auto;margin-bottom: 0px;\"><div class=\"ls-slide\" data-ls=\"duration:6000;transition2d:5;\"><img loading=\"lazy\" decoding=\"async\" width=\"2600\" height=\"270\" src=\"https:\/\/www.iemn.fr\/wp-content\/uploads\/2019\/01\/sliders_news1.jpg\" class=\"ls-bg\" alt=\"\" srcset=\"https:\/\/www.iemn.fr\/wp-content\/uploads\/2019\/01\/sliders_news1.jpg 2600w, https:\/\/www.iemn.fr\/wp-content\/uploads\/2019\/01\/sliders_news1-300x31.jpg 300w, https:\/\/www.iemn.fr\/wp-content\/uploads\/2019\/01\/sliders_news1-768x80.jpg 768w, https:\/\/www.iemn.fr\/wp-content\/uploads\/2019\/01\/sliders_news1-1030x107.jpg 1030w, https:\/\/www.iemn.fr\/wp-content\/uploads\/2019\/01\/sliders_news1-1500x156.jpg 1500w, https:\/\/www.iemn.fr\/wp-content\/uploads\/2019\/01\/sliders_news1-705x73.jpg 705w\" sizes=\"auto, (max-width: 2600px) 100vw, 2600px\" \/><ls-layer style=\"font-size:14px;text-align:left;font-style:normal;text-decoration:none;text-transform:none;font-weight:700;letter-spacing:0px;border-style:solid;border-color:#000;background-position:0% 0%;background-repeat:no-repeat;width:180px;height:30px;left:0px;top:231px;line-height:32px;color:#ffffff;border-radius:6px 6px 6px 6px;padding-left:50px;background-color:rgba(0, 0, 0, 0.57);\" class=\"ls-l ls-ib-icon ls-text-layer\" data-ls=\"minfontsize:0;minmobilefontsize:0;\"><i class=\"fa fa-quote-right\" style=\"color:#ffffff;margin-right:0.8em;font-size:1em;transform:translateY( -0.125em );\"><\/i>ACTUALITES<\/ls-layer><\/div><\/div><\/div><div id='after_layer_slider_1'  class='main_color av_default_container_wrap container_wrap sidebar_right'  ><div class='container av-section-cont-open' ><div class='template-page content  av-content-small alpha units'><div class='post-entry post-entry-type-page post-entry-62629'><div class='entry-content-wrapper clearfix'>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-av_heading-55b170f22db903c7d7d443fffc1c0ae0\">\n#top .av-special-heading.av-av_heading-55b170f22db903c7d7d443fffc1c0ae0{\nmargin:0 0 10px 0;\npadding-bottom:4px;\n}\nbody .av-special-heading.av-av_heading-55b170f22db903c7d7d443fffc1c0ae0 .av-special-heading-tag .heading-char{\nfont-size:25px;\n}\n.av-special-heading.av-av_heading-55b170f22db903c7d7d443fffc1c0ae0 .av-subheading{\nfont-size:15px;\n}\n<\/style>\n<div  class='av-special-heading av-av_heading-55b170f22db903c7d7d443fffc1c0ae0 av-special-heading-h2  avia-builder-el-1  el_after_av_layerslider  el_before_av_hr  avia-builder-el-first'><h2 class='av-special-heading-tag'  itemprop=\"headline\"  >THESE: M. AFNOUCH : Machine Learning Applications in Medical Diagnosis, case study: bone metastasis<\/h2><div class=\"special-heading-border\"><div class=\"special-heading-inner-border\"><\/div><\/div><\/div>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-18u73nj-dad6a947580930e400fc42ba200e80f1\">\n#top .hr.av-18u73nj-dad6a947580930e400fc42ba200e80f1{\nmargin-top:5px;\nmargin-bottom:5px;\n}\n.hr.av-18u73nj-dad6a947580930e400fc42ba200e80f1 .hr-inner{\nwidth:100%;\n}\n<\/style>\n<div  class='hr av-18u73nj-dad6a947580930e400fc42ba200e80f1 hr-custom  avia-builder-el-2  el_after_av_heading  el_before_av_textblock  hr-left hr-icon-no'><span class='hr-inner inner-border-av-border-thin'><span class=\"hr-inner-style\"><\/span><\/span><\/div>\n<section  class='av_textblock_section av-jriy64i8-2f4600354c0449b610997916bbd9b6bc'   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='avia_textblock'  itemprop=\"text\" >\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-13ewzjw-68e036126b913e5028f77311dc66b825\">\n.av_font_icon.av-13ewzjw-68e036126b913e5028f77311dc66b825{\ncolor:#bfbfbf;\nborder-color:#bfbfbf;\n}\n.av_font_icon.av-13ewzjw-68e036126b913e5028f77311dc66b825 .av-icon-char{\nfont-size:60px;\nline-height:60px;\n}\n<\/style>\n<span  class='av_font_icon av-13ewzjw-68e036126b913e5028f77311dc66b825 avia_animate_when_visible av-icon-style- avia-icon-pos-left avia-icon-animate'><span class='av-icon-char' aria-hidden='true' data-av_icon='\ue8c9' data-av_iconfont='entypo-fontello' ><\/span><\/span>\n<p><strong>F. AFNOUCH<br \/>\n<\/strong><\/p>\n<p>Degree: 18 December 2023<br \/>\n<span class=\"titre\">PhD thesis in Signal and Image Processing, Universit\u00e9 Polytechnique Hauts de France<\/span><strong><br \/>\n<\/strong>IEMN Amphitheatre - Central Laboratory - Villeneuve d'Ascq<\/p>\n<\/div><\/section>\n<section  class='av_textblock_section av-jtefqx33-628129dba2299b2ecd65ebfc92eac29d'   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='avia_textblock'  itemprop=\"text\" ><div  class='hr av-kjh3zw-4dff888f744b728a1aca9b3a0971493a hr-default  avia-builder-el-6  avia-builder-el-no-sibling'><span class='hr-inner'><span class=\"hr-inner-style\"><\/span><\/span><\/div>\n<ul>\n<li><\/li>\n<\/ul>\n<h5>Summary:<\/h5>\n<p>Metastases are a group of abnormal cells that develop outside the confines of the original organ and spread to other organs. In particular, bone metastases originate from an organ in the body, such as the breast, lung or prostate, and spread to the bone. Although this disease was discovered over a century ago, it is still not well defined and existing treatments are not very effective, probably because it is difficult and time-consuming to detect. To help doctors, new machine learning techniques offer a solution for improving overall accuracy. This thesis aims to help radiologists systematically detect bone metastases using machine learning algorithms. However, the discovery of methodological biases in studies on the diagnosis of bone metastases and the lack of consensus on the interpretability of machine learning have shifted the focus of this thesis. It now focuses mainly on data collection and on solving the problems of validation and interpretability of machine learning. In order to properly assess the ability of machine learning to detect bone metastases, three experimental studies were conducted. The first proposed a new segmentation approach supported by an attention mechanism for locating bone lesions. The second is a study of machine learning methods for identifying cases of bone metastases. Finally, the last study highlights the lack of robustness of classification using machine learning methods and proposes a method for improving accuracy based on both the CNN and Transformer approaches. The experimental results of this thesis are evaluated on the BM-Seg dataset that we introduced, which is the first reference dataset for the segmentation and classification of bone metastases using CT scans. This open source database was used to improve the reproducibility of machine learning experiments. The results of the various preliminary studies are encouraging and promising.<\/p>\n<h5>Abstract:<\/h5>\n<p>Metastases are a group of abnormal cells that develop outside the confines of the original organ and spread to other organs. In particular, bone metastases originate from an organ in the body, such as the breast, lung or prostate, and spread to the bone. Although this disease was discovered over a century ago, it is still not well defined and existing treatments are not very effective, probably because it is difficult and time-consuming to detect. To help doctors, new machine learning techniques offer a solution for improving overall accuracy. This thesis aims to help radiologists systematically detect bone metastases using machine learning algorithms. However, the discovery of methodological biases in studies on the diagnosis of bone metastases and the lack of consensus on the interpretability of machine learning have shifted the focus of this thesis. It now focuses mainly on data collection and on solving the problems of validation and interpretability of machine learning. In order to properly assess the ability of machine learning to detect bone metastases, three experimental studies were conducted. The first proposed a new segmentation approach supported by an attention mechanism for locating bone lesions. The second is a study of machine learning methods for identifying cases of bone metastases. Finally, the last study highlights the lack of robustness of classification using machine learning methods and proposes a method for improving accuracy based on both the CNN and Transformer approaches. The experimental results of this thesis are evaluated on the BM-Seg dataset that we introduced, which is the first reference dataset for the segmentation and classification of bone metastases using CT scans. This open source database was used to improve the reproducibility of machine learning experiments. The results of the various preliminary studies are encouraging and promising.<\/p>\n<\/div><\/section>","protected":false},"excerpt":{"rendered":"","protected":false},"author":20,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[318],"tags":[],"class_list":["post-62629","post","type-post","status-publish","format-standard","hentry","category-these-2023"],"_links":{"self":[{"href":"https:\/\/www.iemn.fr\/en\/wp-json\/wp\/v2\/posts\/62629","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.iemn.fr\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.iemn.fr\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.iemn.fr\/en\/wp-json\/wp\/v2\/users\/20"}],"replies":[{"embeddable":true,"href":"https:\/\/www.iemn.fr\/en\/wp-json\/wp\/v2\/comments?post=62629"}],"version-history":[{"count":0,"href":"https:\/\/www.iemn.fr\/en\/wp-json\/wp\/v2\/posts\/62629\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.iemn.fr\/en\/wp-json\/wp\/v2\/media?parent=62629"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.iemn.fr\/en\/wp-json\/wp\/v2\/categories?post=62629"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.iemn.fr\/en\/wp-json\/wp\/v2\/tags?post=62629"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}