{"id":25869,"date":"2025-10-09T14:38:59","date_gmt":"2025-10-09T19:38:59","guid":{"rendered":"https:\/\/uwm.edu\/engineering\/?p=25869"},"modified":"2026-01-13T12:20:03","modified_gmt":"2026-01-13T18:20:03","slug":"from-pixels-to-predictions-students-can-uncover-ai-uses-in-the-lab","status":"publish","type":"post","link":"https:\/\/uwm.edu\/engineering\/from-pixels-to-predictions-students-can-uncover-ai-uses-in-the-lab\/","title":{"rendered":"From pixels to predictions: Students can uncover AI uses in the lab"},"content":{"rendered":"\n<p>Cancer research often demands patience \u2013 hours of labeling 3D images, tracing tumor boundaries pixel by pixel before the real work can even begin.&nbsp;<\/p>\n\n\n\n<p>In Associate Professor Mahsa Dabagh\u2019s lab, artificial intelligence is reshaping how researchers predict and treat disease. Dabagh, a biomedical engineering faculty member, studies how blood flows through and feeds tumors and how subtle biological and environmental cues can signal cancer\u2019s next move.&nbsp;<\/p>\n\n\n\n<p>\u201cAI helps us spend less time exploring the data and more time using it to design solutions,\u201d Dabagh said.&nbsp;<\/p>\n\n\n\n<div class=\"uwm-p-notice uwm-p-notice--warning\"><div class=\"uwm-p-notice--content\"><div class=\"uwm-p-notice--title\">Making Waves of Impact<\/div><div class=\"uwm-p-notice--message\">A newcomer to AI seizes the opportunity to fast-track the time-consuming task of segmentation.<\/div><\/div><\/div>\n\n\n\n<figure class=\"alignleft uwm-c-img--left\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"300\" src=\"https:\/\/uwm.edu\/engineering\/wp-content\/uploads\/sites\/537\/2025\/10\/Miles.Wehner.sm_.jpg\" alt=\"a young man with blonde hair sitting behind a computer screen looks at the camera.\" class=\"wp-image-25873\" srcset=\"https:\/\/uwm.edu\/engineering\/wp-content\/uploads\/sites\/537\/2025\/10\/Miles.Wehner.sm_.jpg 300w, https:\/\/uwm.edu\/engineering\/wp-content\/uploads\/sites\/537\/2025\/10\/Miles.Wehner.sm_-150x150.jpg 150w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><figcaption>Biomedical engineering student Miles Smith started by manually labeling medical scans of tumors. Now he\u2019s training AI to do the time-consuming job faster and smarter. &#8216;Learning these methods will be useful no matter where I end up,&#8217; he said.<\/figcaption><\/figure>\n\n\n\n<p>Her team is creating a deep-learning system that doesn\u2019t just scan for tumors \u2013 it builds a virtual model of them. By analyzing MRI and biopsy images alongside detailed patient data such as protein activity, family medical history, and lifestyle factors, the tool can recognize not only what a tumor looks like, but how it might behave.&nbsp;<\/p>\n\n\n\n<p>\u201cResearch in the last decade shows that one data source is rarely enough for accurate prediction,\u201d Dabagh said. \u201cAI\u2019s strength is in pulling together all those layers of information to create a more complete picture.\u201d&nbsp;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-undergraduate-contributions-nbsp\"><strong>Undergraduate contributions<\/strong>&nbsp;<\/h3>\n\n\n\n<p>Biomedical engineering senior Miles Wehner&#8217;s summer role in Dabagh&#8217;s lab was to work on the first step of the project, labeling tumors in MRI scans, a process that can take an hour per patient. Going slice by slice through hundreds of 3D images, Wehner had to carefully trace and label the tumor areas pixel by pixel.&nbsp;&nbsp;<\/p>\n\n\n\n<p>He soon saw the potential for AI to speed things up. Now he&#8217;s building a platform that uses both his own segmented scans and publicly available datasets to automate the entire process, bringing AI into the process earlier than the final predictions.&nbsp;<\/p>\n\n\n\n<p>\u201cIt\u2019s still a lot of work to train our models,\u201d Dabagh said, \u201cbut it\u2019s worth it. It can improve clinicians\u2019 predictions and give them personalized guidance for each patient\u2019s treatment. That means saving more lives.\u201d&nbsp;<\/p>\n\n\n\n<p>For Wehner, who only started coding through a summer program at 51ÁÔÆæ that introduces students to AI and programming, the research opened new doors. The program, Cyberinfrastructure Comprehensive, Applied and Tangible Summer School, or <a href=\"https:\/\/uwm.edu\/news\/students-learn-to-apply-high-performance-computing-to-research-through-new-grant\/\">CIberCATSS<\/a>, is funded by the National Science Foundation through 2026 and students with a faculty mentor can be admitted.&nbsp;<\/p>\n\n\n\n<p>\u201cThey say AI is the future. Learning these methods will be useful no matter where I end up,\u201d he said. \u201cDoing this research helped me see what\u2019s possible.&#8221;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Cancer research often demands patience \u2013 hours of labeling 3D images, tracing tumor boundaries pixel by pixel before the real work can even begin.&nbsp; In Associate Professor Mahsa Dabagh\u2019s lab, artificial intelligence is reshaping how researchers predict and treat disease. &hellip;<\/p>\n","protected":false},"author":836,"featured_media":25870,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","uwm_wg_additional_authors":[]},"categories":[156,94,66,25],"tags":[163],"class_list":["post-25869","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai","category-biomedical-health","category-milwaukee-engineer","category-news","tag-data-into-solutions"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.3 (Yoast SEO v27.3) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>College of Engineering &amp; 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