Author
Cancer Research UK Scotland InstituteORCID 0000-0003-0506-2801Unclaimed ORCID profile · follow to get notified of new preprints
6 papers
Recent·Most cited
Adam S. Bryce, Silvia Martinelli, Leonor Schubert Santana…·David Kuang-Fu Chang
This study identifies architectural subtypes of PDAC using morphospatial profiling of CAFs, showing improved prognostication and chemotherapy response prediction.
Hannah L. Williams, Nicolas Poulain, Ian R. Powley…·John P. C. Le Quesne
This study uses spatial transcriptomics to analyze gene expression in lung adenocarcinoma morphologies, revealing plasticity and gene expression trajectories linked to lethal growth patterns.
Kai Rakovic, Alexandrina Pancheva, Adalberto Claudio Quiros…·John P. C. Le Quesne
This paper uses self-supervised AI to identify under-appreciated stromal morphologies in lung adenocarcinoma linked to poor prognosis, independent of current clinical schemas.
Lucas Farndale, Kai Rakovic, Ian R. Powley…·Marco Sereno
This paper introduces a self-supervised learning framework to analyze multiplex immunofluorescence images, revealing novel prognostic spatial patterns in cancer tissues.
Farzaneh Seyedshahi, Kai Rakovic, Nicolas Poulain…·John P. C. Le Quesne
This paper uses self-supervised learning on 3446 H&E-stained mesothelioma slides to create a histomorphological atlas, achieving high predictive performance and revealing molecular insights.
Rachel L. Pennie, David Mason, Kai Rakovic…·John P. C. Le Quesne
PixlMap is a deep learning method for cellular phenotyping in multiplex immunofluorescence images that uses nuclear segmentation alone to classify cells without requiring full cell boundaries.