Large Multimodal Model based Standardisation of Pathology Reports with Confidence and their Prognostic Significance

no code implementations • 3 May 2024 • Ethar Alzaid, Gabriele Pergola, Harriet Evans, David Snead, Fayyaz Minhas

In this work, we present a practical approach based on the use of large multimodal models (LMMs) for automatically extracting information from scanned images of pathology reports with the goal of generating a standardised report specifying the value of different fields along with estimated confidence about the accuracy of the extracted fields.

TIAViz: A Browser-based Visualization Tool for Computational Pathology Models

1 code implementation • 15 Feb 2024 • Mark Eastwood, John Pocock, Mostafa Jahanifar, Adam Shephard, Skiros Habib, Ethar Alzaid, Abdullah Alsalemi, Jan Lukas Robertus, Nasir Rajpoot, Shan Raza, Fayyaz Minhas

Throughout the development of a machine learning (ML) model in digital pathology, it is crucial to have flexible, openly available tools to visualize models, from their outputs and predictions to the underlying annotations and images used to train or test a model.

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