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Built UNI and CONCH, the pathology foundation models that let AI read whole-slide images across cancer types.
Faisal Mahmood's laboratory developed CLAM for weakly supervised whole-slide learning, TOAD for predicting tumour origin, and the UNI and CONCH foundation models trained on more than 100 million pathology images, which set the benchmark for general-purpose computational pathology. His group also builds multimodal models integrating histology with genomics for prognosis and is a leading academic voice on AI in pathology.
| Title | Journal | Year |
|---|---|---|
| Towards a general-purpose foundation model for computational pathology (UNI) | Nature Medicine | 2024 |
| AI-based pathology predicts origins for cancers of unknown primary | Nature | 2021 |
| Data-efficient and weakly supervised computational pathology on whole-slide images | Nature Biomedical Engineering | 2021 |
| AlphaFold 2: predicting protein structures to near-experimental accuracy | Nature | 2021 |
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 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.
Shares Pathology & radiology foundation models and the tag pathology.