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New directions for deep learning in cancer research through concept explainability and virtual experimentation.

CORDIS · observation · Publication date unknown

New directions for deep learning in cancer research through concept explainability and virtual experimentation. Deep learning (DL) is rapidly transforming cancer research and oncology. DL can extract subtle visual features from preclinical and clinical image data. In my junior research group, I have developed end-to-end DL methods to predict molecular biomarkers and clinical outcomes directly from histopathology slides. Because histopathology slides are ubiquitously available for any patient with a solid tumor, DL is a broad tool for translational studies, enabling researchers to extract molecular information and make predictions about clinical outcome. However, the potential of DL in cancer research is fundamentally limited because it is purely descriptive and, in many cases, a black-box system. Also, DL is currently disjoint from the vast amount of biological mechanistic knowledge in cancer research, and

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recordType

award

status

SIGNED

region

EU

value

1498750

unit

EUR