{"id":2137,"date":"2026-08-20T17:19:43","date_gmt":"2026-08-20T15:19:43","guid":{"rendered":"https:\/\/sbe.eng.cu.edu.eg\/?p=2137"},"modified":"2026-08-20T17:56:32","modified_gmt":"2026-08-20T15:56:32","slug":"faimi-miccai-sbme26","status":"publish","type":"post","link":"https:\/\/sbe.eng.cu.edu.eg\/en\/blog\/2026\/08\/20\/faimi-miccai-sbme26\/","title":{"rendered":"Trustworthy Breast Cancer AI Research Accepted at FAIMI @ MICCAI"},"content":{"rendered":"<p><\/p>\n<div>\n<p>  <!-- Main Announcement --><\/p>\n<div lang=\"en\" style=\"font-size:1.2em;line-height:1.9\">\n    We are proud to celebrate the acceptance of the research paper <strong>\u201cMultiple Shortcut Pathways in Mammography-Based Breast Cancer Risk Prediction\u201d<\/strong> at the <strong style=\"color:#d99f53\">FAIMI 2026 Workshop on Fairness of AI in Medical Imaging<\/strong>, held in conjunction with <strong>MICCAI 2026<\/strong>.\n  <\/div>\n<p>  <!-- Image --><\/p>\n<div style=\"text-align:center;margin:25px 0\">\n    <a href=\"http:\/\/sbe.eng.cu.edu.eg\/wp-content\/uploads\/sites\/4\/sbe2\/2026\/SIG-FAIMI-logo.jpeg\" target=\"_blank\" rel=\"noopener\"><br \/>\n      <img decoding=\"async\" style=\"max-width:50%;border:4px solid #d99f53;margin-bottom:10px\" src=\"http:\/\/sbe.eng.cu.edu.eg\/wp-content\/uploads\/sites\/4\/sbe2\/2026\/SIG-FAIMI-logo.jpeg\" alt=\"Multiple Shortcut Pathways in Mammography-Based Breast Cancer Risk Prediction - FAIMI 2026\" \/><br \/>\n    <\/a>\n  <\/div>\n<p>  <!-- Authors --><\/p>\n<div lang=\"en\" style=\"font-size:1.2em;line-height:1.9\">\n    The paper is authored by <strong>Judy Ashmawy, Habiba Mohamed, and Maya Hussien<\/strong>, under the supervision of <strong>Professor Dr. Tamer Basha<\/strong> and <strong>Eng. Alaa Melek<\/strong>, Assistant Lecturer at <strong>SBME<\/strong>. The work originated from the students\u2019 graduation project and has now progressed to publication at an international scientific venue.\n  <\/div>\n<p>  <!-- Research --><\/p>\n<div lang=\"en\" style=\"font-size:1.2em;line-height:1.9;margin-top:20px\">\n    The research investigates an important challenge in <strong>AI-based breast cancer risk prediction from mammography<\/strong>: whether highly performing deep learning models are making predictions based on clinically meaningful imaging features or relying on unintended <strong>shortcut signals<\/strong>. Using techniques including <strong>occlusion analysis, background perturbation experiments, and representation analysis<\/strong>, the study identifies multiple shortcut pathways that can influence model predictions, highlighting the importance of <strong>interpretability, fairness, and clinically reliable AI<\/strong> in medical imaging.\n  <\/div>\n<p>  <!-- FAIMI \/ MICCAI Significance --><\/p>\n<div lang=\"en\" style=\"font-size:1.2em;line-height:1.9;margin-top:20px\">\n    <strong>FAIMI \u2013 Fairness of AI in Medical Imaging<\/strong> is a specialized workshop held in conjunction with <strong>MICCAI<\/strong>, the flagship international conference in <strong>medical image computing and computer-assisted intervention<\/strong>. FAIMI brings together researchers working to understand and mitigate <strong>bias, fairness, robustness, and generalizability challenges in medical AI<\/strong>, with the goal of supporting safer and more equitable translation of artificial intelligence into clinical practice.\n  <\/div>\n<p>  <!-- Congratulations --><\/p>\n<div lang=\"en\" style=\"font-size:1.2em;line-height:1.9;margin-top:20px\">\n    The <strong>Systems and Biomedical Engineering Department<\/strong> proudly congratulates the research team on this achievement and wishes them continued success as they present their work to the international medical imaging research community at <strong style=\"color:#d99f53\">MICCAI 2026 in Strasbourg, France<\/strong>.\n  <\/div>\n<p><!-- Paper Link --><\/p>\n<div lang=\"en\" style=\"font-size:1.1em;line-height:1.9;margin-top:20px\">\n  <strong>Read the paper:<\/strong><br \/>\n  <a href=\"https:\/\/openreview.net\/forum?id=uG64jemYMl\" target=\"_blank\" rel=\"noopener\"><br \/>\n    Multiple Shortcut Pathways in Mammography-Based Breast Cancer Risk Prediction<br \/>\n  <\/a>\n<\/div>\n<\/div>\n<p><\/p>","protected":false},"excerpt":{"rendered":"<p>We are proud to celebrate the acceptance of the research paper \u201cMultiple Shortcut Pathways in Mammography-Based Breast Cancer Risk Prediction\u201d at the FAIMI 2026 Workshop on Fairness of AI in Medical Imaging, held in conjunction with MICCAI 2026. The paper is authored by Judy Ashmawy, Habiba Mohamed, and Maya Hussien, under the supervision of Professor [&hellip;]<\/p>\n","protected":false},"author":7,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[28,48,45,47],"tags":[],"class_list":["post-2137","post","type-post","status-publish","format-standard","hentry","category-conferences","category-generalnews","category-homenews","category-studentnews"],"_links":{"self":[{"href":"https:\/\/sbe.eng.cu.edu.eg\/en\/wp-json\/wp\/v2\/posts\/2137","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sbe.eng.cu.edu.eg\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/sbe.eng.cu.edu.eg\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/sbe.eng.cu.edu.eg\/en\/wp-json\/wp\/v2\/users\/7"}],"replies":[{"embeddable":true,"href":"https:\/\/sbe.eng.cu.edu.eg\/en\/wp-json\/wp\/v2\/comments?post=2137"}],"version-history":[{"count":8,"href":"https:\/\/sbe.eng.cu.edu.eg\/en\/wp-json\/wp\/v2\/posts\/2137\/revisions"}],"predecessor-version":[{"id":2146,"href":"https:\/\/sbe.eng.cu.edu.eg\/en\/wp-json\/wp\/v2\/posts\/2137\/revisions\/2146"}],"wp:attachment":[{"href":"https:\/\/sbe.eng.cu.edu.eg\/en\/wp-json\/wp\/v2\/media?parent=2137"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sbe.eng.cu.edu.eg\/en\/wp-json\/wp\/v2\/categories?post=2137"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sbe.eng.cu.edu.eg\/en\/wp-json\/wp\/v2\/tags?post=2137"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}