Mapping the histomorphological landscape across mouse tumours with self-supervised AI models

Background

Histological assessment of mouse tumor phenotypes plays a crucial role in functional studies. Traditionally, this analysis has relied on manual examination, which is insufficient for modern experimental scales that require the evaluation and quantification of hundreds or even thousands of histology slides. Self-supervised AI models in pathology present an opportunity to significantly improve the efficiency and accuracy of phenotypic assessments in mouse models.

Our group has recently developed the Histomorphological Phenotype Learning (HPL) framework, a self-supervised AI tool designed to detect recurring patterns in pathology slides. This approach has already been successfully applied to several human cancers, uncovering a landscape of previously underappreciated tumor phenotypes. Building on this success, we aim to apply self-supervised AI models to systematically map histologic patterns across multiple cancer types in mouse models. These AI-driven models will not only enhance our understanding of tumor phenotypes but will also facilitate rapid and scalable quantification of histological data, applicable to future experiments as soon as the images are generated.

Research Question

The primary goal of this project is to develop self-supervised AI foundation models using mouse pathology slides. These models will be trained to predict molecular characteristics, including mutation profiles, transcriptomic signatures, and proteomic data. Furthermore, we will integrate these multimodal data with human datasets for the purpose of target validation and understanding disease mechanisms. Through this work, we seek to advance the field of pathology by enabling more precise and scalable histological analysis in both preclinical and clinical settings.

Skills/techniques that will be gained

1.     Programming skills in training, validation and testing state-of-the-art deep learning models.

2.     State-of-the-art methods in computer vision and medical image analysis

3.     Biological interpretation and histological assessment of H&E and mIF images

4.     Broad understanding of the translational value of cutting-edge data and technology in cancer sciences. 

About us

The CRUK Scotland Institute where the lab is based is a world leading cancer research institute, situated in the vibrant city of Glasgow in Scotland and hosting approximately 60 doctoral researchers at any one time. It has an excellent reputation for fundamental cancer research, including world-class metabolism studies and renowned in vivo modelling of tumour growth and metastasis.

To apply, and for further details on the application process, please click ‘Institute website’. Please do not email your CV.