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This year鈥檚 International Symposium on Biomedical Imaging (ISBI) was held in Houston, Texas鈥攈ome to NASA鈥檚 Johnson Space Center and the sprawling Texas Medical Center. With sunny skies and wide Texan roads welcoming us, we arrived two days early to make the most of the trip before diving into 4 days of cutting-edge research. We kicked off our trip with an NBA game at the Toyota Center and followed it up with a tour of the NASA Space Center. Walking through the rocket park and seeing the Saturn V rocket and the SpaceX Falcon 9 Booster up close was an unforgettable reminder of how science can propel us (literally!) to new frontiers. Read MoreLink opens in a new window

The Bigpicture project is an initiative aimed at transforming the field of pathology through the creation of a comprehensive, high-quality digital repository of three million pathology images. Funded by the EU Innovative Medicines Initiative (IMI), Bigpicture brings together a consortium of leading academic institutions, research organisations, and industry partners from across Europe and beyond. The project鈥檚 mission is to develop an open-access platform that leverages artificial intelligence and machine learning to analyse vast amounts of pathology data, advancing research, diagnostics, and treatment in healthcare. By fostering collaboration and innovation, Bigpicture envisions a future where digital pathology becomes a cornerstone of precision medicine, enabling faster, more accurate diagnoses and personalised patient care. Read MoreLink opens in a new window

We are delighted to announce that one of the TIA Centre鈥檚 PhD students, Ruoyu Wang, has officially passed their PhD viva last week. Their research entitled Computational Pathology Algorithms for Precision Oncology has been a fantastic contribution, and we couldn鈥檛 be prouder. Find a summary on their research below:-

Ruoyu's thesis focused on developing artificial intelligence (AI) tools to analyse routine histology slides and support personalised cancer care. Specifically, their research explored how deep learning can be used to predict clinically significant molecular biomarkers, stratify patients, and uncover novel tumour microenvironment (TME) patterns directly from digitised whole slide images (WSIs) of routine histology slides. These AI-driven approaches offer scalable, cost-effective alternatives to traditional molecular testing and contribute to the integration of AI into precision oncology, ultimately advancing more tailored and efficient cancer treatment strategies.