AI in Oncology · AI Models

Clinical Histopathology Imaging Evaluation Foundation model: trained on 60,530 whole-slide images across 19 anatomical sites and validated on 19,491 slides from 24 hospitals for cancer detection, tumour-origin prediction, genomic profiling and survival.

Vision-language foundation model for pathology: an image encoder and a text encoder trained together on 1.17 million histopathology image–caption pairs, enabling zero-shot classification and image–text retrieval without labelled slides.

Deep-learning variant caller that turns aligned sequencing reads into pileup images and classifies genotypes with a CNN; widely used for germline calling, with the companion DeepSomatic extending the approach to tumour–normal somatic variants.

1.1-billion-parameter ViT-g/14 pathology encoder trained on more than 500,000 H&E slides (hundreds of millions of tiles), released under Apache-2.0 — one of the few large pathology foundation models with a permissive licence.

Whole-slide foundation model with 1.3 billion parameters, pretrained on 1.3 billion tiles from 171,189 slides of real-world clinical data; pairs a DINOv2 tile encoder with a LongNet slide encoder that reasons over an entire slide.

Successor to Virchow: ViT-H/14 pretrained on 3.1 million whole-slide images from about 225,000 patients across 45 countries, at mixed magnifications (5×–40×), with pathology-specific augmentations.

General-purpose self-supervised vision encoder for H&E histopathology tiles, pretrained on more than 100 million tiles from over 100,000 whole-slide images; the reference foundation model for pathology feature extraction.

632-million-parameter vision transformer pretrained on 1.5 million whole-slide images from about 100,000 patients — the largest pathology pretraining set at its release — and used to build a pan-cancer detection model covering 17 cancer types, including rare ones.

The first AI software for pathology authorised by the US FDA (De Novo, September 2021): flags prostate biopsy whole-slide images likely to contain cancer so the pathologist reviews the suspicious region.

Classifies the pathogenicity of every possible single amino-acid substitution in the human proteome — 71 million missense variants — by fine-tuning AlphaFold on population variant frequencies; a resource for interpreting variants of uncertain significance.