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DESCRIPTION:Pathology data\, primarily consisting of slides and diagnostic 
 reports\, inherently contain knowledge pivotal for advancing data-driven b
 iomedical research and clinical practice. However\, the hidden and fragmen
 ted nature of this knowledge across various data modalities not only hinde
 rs its computational utilization but also impedes the effective integratio
 n of AI technologies in the domain of pathology. To systematically organiz
 e pathology knowledge for computational use\, we propose PathoGraph\, a re
 presentation method capable of comprehensively and structurally capturing 
 multi-scale disease characteristics alongside pathologists’ expertise in
  a graph-based format. Furthermore\, by leveraging existing pathology imag
 e recognition techniques\, we achieved large-scale automated construction 
 of PathoGraph\, applied it to enhance the performance of downstream deep l
 earning models\, and presented two illustrative use cases that highlight i
 ts clinical potential. Collectively\, we believe our efforts lay a critica
 l foundation for constructing pathology knowledge graphs\, thereby advanci
 ng AI-driven pathology practice.\n\nAgenda: \n6:30 - 7:00 Social half hour
  to grab food and drink\n\n7:00 - 8:00 Technical talk\n\nRoom: Mann Hall\,
  Bldg: Medical Sciences Building\, 300 3rd Ave SW\, Rochester\, Minnesota\
 , United States\, 55902\, Virtual: https://events.vtools.ieee.org/m/472672
LOCATION:Room: Mann Hall\, Bldg: Medical Sciences Building\, 300 3rd Ave SW
 \, Rochester\, Minnesota\, United States\, 55902\, Virtual: https://events
 .vtools.ieee.org/m/472672
ORGANIZER:pramanik.leena@ieee.org
SEQUENCE:23
SUMMARY:March Talk: AI-Driven Pathology- Converting Knowledge to Graph Repr
 esentations (HYBRID)
URL;VALUE=URI:https://events.vtools.ieee.org/m/472672
X-ALT-DESC:Description: <br /><p style="text-align: justify\;">Pathology da
 ta\, primarily consisting of slides and diagnostic reports\, inherently co
 ntain knowledge pivotal for advancing data-driven biomedical research and 
 clinical practice. However\, the hidden and fragmented nature of this know
 ledge across various data modalities not only hinders its computational ut
 ilization but also impedes the effective integration of AI technologies in
  the domain of pathology. To systematically organize pathology knowledge f
 or computational use\, we propose PathoGraph\, a representation method cap
 able of comprehensively and structurally capturing multi-scale disease cha
 racteristics alongside pathologists&rsquo\; expertise in a graph-based for
 mat. Furthermore\, by leveraging existing pathology image recognition tech
 niques\, we achieved large-scale automated construction of PathoGraph\, ap
 plied it to enhance the performance of downstream deep learning models\, a
 nd presented two illustrative use cases that highlight its clinical potent
 ial. Collectively\, we believe our efforts lay a critical foundation for c
 onstructing pathology knowledge graphs\, thereby advancing AI-driven patho
 logy practice.</p><br /><br />Agenda: <br /><p><em>6:30 - 7:00</em>&nbsp\;
 Social half hour to grab food and drink</p>\n<p><em>7:00 - 8:00</em>&nbsp\
 ;Technical talk</p>\n<p>&nbsp\;</p>
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