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VERSION:2.0
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CALSCALE:GREGORIAN
X-WR-CALNAME:Imaging Science & Engineering Seminar | Quing Zhu
X-WR-TIMEZONE:Central Time (US & Canada)
BEGIN:VEVENT
DTSTAMP:20260611T004451Z
UID:tag:localist.com\,2008:EventInstance_50789368464891
DTSTART:20251003T133000Z
DTEND:20251003T143000Z
DESCRIPTION:Optical Imaging Techniques and Machine-Learning Models for Diag
 nosis of Breast\, Ovarian\, and Endometrial Cancers\n\nThis talk will focu
 s on our recent progress in cancer imaging across three major research are
 as:\nUltrasound-guided diffuse optical imaging combined with machine learn
 ing models to improve the accurate diagnosis of breast cancer and reduce u
 nnecessary breast biopsies\;\nDual-modality photoacoustic and ultrasound i
 maging with machine learning models for accurate diagnosis of ovarian canc
 er and reduction of unnecessary surgeries\;\nOptical coherence tomography 
 (OCT) integrated with machine learning models for potential endometrial ca
 ncer screening and accurate diagnosis.\n\nIn our first study on ultrasound
 -guided diffuse optical imaging\, we completed a 5-year clinical trial inv
 olving 300 patients. The results showed a 25% reduction in unnecessary ben
 ign biopsies. A fusion machine learning model demonstrated its potential t
 o enhance radiologists’ performance in further reducing these unnecessar
 y procedures.  In our second study on dual-modality photoacoustic and ultr
 asound imaging\, recent diagnostic and machine learning results from over 
 70 patients demonstrated significantly improved sensitivity and specificit
 y in ovarian lesion diagnosis.  In the third study\, we demonstrated that 
 optical coherence tomography (OCT)\, combined with machine learning algori
 thms\, can be used as a screening and diagnostic tool for endometrial canc
 er.\nFinally\, I will highlight our next steps toward advancing these tech
 nologies and AI tools to support robust clinical translation.
GEO:38.648794;-90.30145
LOCATION:Preston M. Green Hall\, Rodin Auditorium room 0120
SUMMARY:Imaging Science & Engineering Seminar | Quing Zhu
URL;VALUE=URI:https://happenings.washu.edu/event/imaging-science-engineerin
 g-seminar-quing-zhu
CATEGORIES:Lectures & Presentations
CATEGORIES:Seminar/Colloquia
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