BEGIN:VCALENDAR VERSION:2.0 PRODID:-//UC Irvine//CML Seminars//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:AI-based multimodal data fusion for outcome prediction in onco logy X-WR-TIMEZONE:America/Los_Angeles BEGIN:VTIMEZONE TZID:America/Los_Angeles BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20070311T020000 RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20071104T020000 RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU END:STANDARD END:VTIMEZONE BEGIN:VEVENT UID:2024-10-28-jana-lipkova@cml.ics.uci.edu DTSTAMP:20241028T000000Z SEQUENCE:57690 DTSTART;TZID=America/Los_Angeles:20241028T130000 DTEND;TZID=America/Los_Angeles:20241028T140000 SUMMARY:[CML Seminar] Jana Lipkova: AI-based multimodal data fusion for out come prediction in oncology LOCATION:Donald Bren Hall 4011 DESCRIPTION:Jana Lipkova\, Assistant Professor\, Department of Pathology\, School of Medicine\, University of California\, Irvine\n\nTitle: AI-based multimodal data fusion for outcome prediction in oncology\n\nAbstract: In oncology\, the patient state is characterized by a spectrum of diverse med ical data\, each providing unique insights. The vast amount of data\, howe ver\, makes it difficult for experts to adequately assess patient prognosi s under the multimodal context. We present a deep learning-based multimoda l framework for integration of radiology\, histopathology\, and genomics d ata to improve patient outcome prediction. The framework does not require annotations\, tumor segmentation\, or hand-crafted features and can be eas ily applied to larger cohorts and diverse disease models. The feasibility of the model is tested on two external independent cohorts\, including gli oma and non-small cell lung cancer\, indicating benefits of multimodal dat a integration for patient risk stratification\, outcome prediction\, and p rognostic biomarker exploration.\n\nhttps://cml.ics.uci.edu/seminars/2024- 10-28-jana-lipkova X-ALT-DESC;FMTTYPE=text/html:Jana Lipkova\, Assistant Pr ofessor\, Department of Pathology\, School of Medicine\, University of Cal ifornia\, Irvine

Title: AI-based multimodal data fusion for outcome prediction in oncology

Abstract: In oncology\, the p atient state is characterized by a spectrum of diverse medical data\, each providing unique insights. The vast amount of data\, however\, makes it d ifficult for experts to adequately assess patient prognosis under the mult imodal context. We present a deep learning-based multimodal framework for integration of radiology\, histopathology\, and genomics data to improve p atient outcome prediction. The framework does not require annotations\, tu mor segmentation\, or hand-crafted features and can be easily applied to l arger cohorts and diverse disease models. The feasibility of the model is tested on two external independent cohorts\, including glioma and non-smal l cell lung cancer\, indicating benefits of multimodal data integration fo r patient risk stratification\, outcome prediction\, and prognostic biomar ker exploration.

https://cml.ics.uci.edu/seminars/2024-10-28-jana-lipkova< /a> URL:https://cml.ics.uci.edu/seminars/2024-10-28-jana-lipkova END:VEVENT END:VCALENDAR