BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Sabre//Sabre VObject 4.5.8//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Europe/Zurich X-LIC-LOCATION:Europe/Zurich TZURL:http://tzurl.org/zoneinfo/Europe/Zurich BEGIN:DAYLIGHT TZOFFSETFROM:+0100 TZOFFSETTO:+0200 TZNAME:CEST DTSTART:19810329T020000 RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=-1SU END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0200 TZOFFSETTO:+0100 TZNAME:CET DTSTART:19961027T030000 RRULE:FREQ=YEARLY;BYMONTH=10;BYDAY=-1SU END:STANDARD END:VTIMEZONE BEGIN:VEVENT UID:news498@dbe.unibas.ch DTSTAMP;TZID=Europe/Zurich:20260313T110834 DTSTART;TZID=Europe/Zurich:20260521T163000 SUMMARY:AI Foundation Modeling for Oncologic Pathology DESCRIPTION:AbstractPathology practice and biological research generate lar ge volumes of multimodal data\, ranging from tissue sections stained with various markers to DNA assays and\, recently\, spatial transcriptomics. Ye t\, H&E-stained tissue remains the gold standard for studying many disease s\, including cancer. This underscores the importance of developing genera l-purpose AI models for histology to advance precision medicine\, patient stratification\, and prognostication\, among others. In this talk\, I will explore two key questions: How can we develop universal\, agentic\, and a ctionable models for histology?\, and How can we effectively leverage thes e models for diagnosis and prognosis\, and biomarker discovery? I will be discussing these points from the perspective of studies published during m y postdoctoral time at Harvard Medical School\, with projects such as UNI (Nature Medicine)\, Threads (Nature Cancer)\, SlideSeek Tangle (CVPR)\, Ma deleine (ECCV)\, and HEST (NeurIPS) as well as from the point of view of d rug-induced toxicity\, with studies such as TRACE and GEESE. \\r\\nBioske tchGuillaume Jaume is a tenure-track assistant professor in the Department of Oncology at The University of Lausanne (UNIL). He completed a postdoct oral fellowship at Harvard Medical School and Brigham & Women's Hospital i n the group of Prof. Faisal Mahmood. He obtained his Ph.D. in Electrical a nd Electronic Engineering from EPFL in collaboration with IBM Research and ETH Zurich in 2022. Guillaume’s research focuses on AI for pathology an d oncology\, with the goal of integrating AI tools into both the clinical and research facets of pathology. His research involves two main objective s: first\, enhancing the representation learning of tissue by developing g eneral-purpose foundation models for pathology and oncology\; and second\, integrating AI tools in drug development to improve drug safety assessmen t\, detect toxicity\, and discover safety biomarkers. X-ALT-DESC:

Abstract
Pathology practice and biologi cal research generate large volumes of multimodal data\, ranging from tiss ue sections stained with various markers to DNA assays and\, recently\, sp atial transcriptomics. Yet\, H&\;E-stained tissue remains the gold stan dard for studying many diseases\, including cancer. This underscores the i mportance of developing general-purpose AI models for histology to advance precision medicine\, patient stratification\, and prognostication\, among others. In this talk\, I will explore two key questions: How can we devel op universal\, agentic\, and actionable models for histology?\, and How ca n we effectively leverage these models for diagnosis and prognosis\, and b iomarker discovery? I will be discussing these points from the perspective of studies published during my postdoctoral time at Harvard Medical Schoo l\, with projects such as UNI (Nature Medicine)\, Threads (Nature Cancer)\ , SlideSeek Tangle (CVPR)\, Madeleine (ECCV)\, and HEST (NeurIPS) as well as from the point of view of drug-induced toxicity\, with studies such as TRACE and GEESE.
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Biosketch
Guil laume Jaume is a tenure-track assistant professor in the Department of Onc ology at The University of Lausanne (UNIL). He completed a postdoctoral fe llowship at Harvard Medical School and Brigham &\; Women's Hospital in the group of Prof. Faisal Mahmood. He obtained his Ph.D. in Electrical and Electronic Engineering from EPFL in collaboration with IBM Research and E TH Zurich in 2022. Guillaume’s research focuses on AI for pathology and oncology\, with the goal of integrating AI tools into both the clinical an d research facets of pathology. His research involves two main objectives: first\, enhancing the representation learning of tissue by developing gen eral-purpose foundation models for pathology and oncology\; and second\, i ntegrating AI tools in drug development to improve drug safety assessment\ , detect toxicity\, and discover safety biomarkers.

DTEND;TZID=Europe/Zurich:20260521T173000 END:VEVENT END:VCALENDAR