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Showed that deep learning can read genetic features like microsatellite instability directly from routine pathology slides.
Jakob Nikolas Kather led the 2019 Nature Medicine study showing a deep-learning model can predict microsatellite instability from standard H&E slides in gastrointestinal cancer, opening the field of inferring molecular biomarkers from routine histology, and subsequent pan-cancer work predicting mutations and outcomes. He leads clinical AI research in Dresden, evaluates large language models for oncology, and is a practising GI oncologist.
| Title | Journal | Year |
|---|---|---|
| Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer | Nature Medicine | 2019 |
| Pan-cancer image-based detection of clinically actionable genetic alterations | Nature Cancer | 2020 |
Shares Pathology & radiology foundation models, Digital pathology & AI and the tags ai, pathology.
Shares Pathology & radiology foundation models, Digital pathology & AI and the tags ai, pathology.
Shares Pathology & radiology foundation models, Digital pathology & AI and the tag pathology.
Shares Pathology & radiology foundation models and the tag pathology.
Shares NCT/UCC Dresden, University Hospital Carl Gustav Carus, Colorectal cancer.
Shares Pathology & radiology foundation models and the tag pathology.
Shares Pathology & radiology foundation models, Digital pathology & AI and the tag pathology.
Shares Pathology & radiology foundation models and the tag pathology.