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Deep Learning for Precision Oncology

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Location: MB3.17, Computer Science

Speaker: Jakob Nikolas Kather (University Hospital Aachen & German Cancer Center Heidelberg, Germany)

Abstract: Precision oncology requires molecular and genetic testing of tumor tissue. For many tests, widespread implementation into clinical practice is limited because these biomarkers are costly, require significant expertise and are limited by tissue availability. However, virtually every cancer patient gets a biopsy as part of the diagnostic workup and this tissue is routinely stained with hematoxylin and eosin (HE). We have developed a deep learning-based technology to predict molecular features, prognosis and markers for treatment response directly from routine histology. This talk will summarize the state of the art in deep learning histopathology, demonstrate emerging use cases and discuss the clinical implications of deep learning based molecular testing of solid tumors.


Short Bio: Jakob Nikolas Kather is a clinician-scientist at University Hospital Aachen and the German Cancer Center Heidelberg, Germany. He leads a junior research group focused on deep-learning based prediction of treatment response in gastrointestinal cancer.

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