Dr Bifulco talks to ecancer at ESMO 2024 about GigaPath - an open-weight billion-parameter AI model based on a novel vision transformer architecture which can be used for cancer mutation prediction and tumour microenvironment analysis.

GigaPath excels in long-context modelling of gigapixel pathology slides, by distilling varied local pathological structures and integrating global signatures across the whole slide.

The team compared GigaPath H&E molecular prediction with competing methods HIPT, CtransPath, REMEDIS, across three tasks: lung adeno 5-gene (EGFR, FAT1, KRAS, TP53, LRP1B), pan-cancer 5-gene, and tumour mutational burden prediction.

Dr Bifulco notes that GigaPath could potentially be applicable to broader biomedical domains for efficient self-supervised learning from high-resolution images, including applications leveraging the long-context modelling features of the model to deconvolute emerging spatial biology datasets to drive a personalized and comprehensive characterization the tumour microenvironment.

To accelerate research progress in digital pathology, the team made GigaPath fully open-weight, including source code and pretrained model weights