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CAI4CAI members are leading the organization of the cross-modality Domain Adaptation challenge (crossMoDA) for medical image segmentation Challenge, which runs as an official challenge during the Medical Image Computing and Computer Assisted Interventions (MICCAI) 2021 conference.

King’s College London, School of Biomedical Engineering & Imaging Sciences and Moon Surgical announced a new strategic partnership to develop Machine Learning applications for Computer-Assisted Surgery, which aims to strengthen surgical artificial intelligence (AI), data and analytics, and accelerate translation from King’s College London research into clinical usage.

Yijing will develop a 3D functional optical imaging system for guiding brain tumour resection. Yijing presenting her work at New Scientist Live. She will engineer two emerging modalities, light field and multispectral imaging into a compact device, and develop novel image reconstruction algorithm to produce and display high-dimensional images.

Miguel will collaborate with Fang-Yu Lin and Shu Wang to create activities to engage school students with ultrasound-guidance intervention and fetal medicine. In the FETUS project, they will develop interactive activities with 3D-printed fetus, placenta phantoms as well as the integreation of a simulator that explain principles of needle enhancement of an ultrasound needle tracking system.

A great milestone today for Luis Garcia Peraza Herrera who passed his PhD viva with minor corrections! His thesis is entitled “Deep Learning for Real-time Image Understanding in Endoscopic Vision”.

This project will evaluate and contribute to the optimisation of a pre-CE-mark and pre-commercial HSI medical device for intraoperative surgical guidance in brain tumour surgery. First-in-patient clinical study using the hyperspectral imaging device (spine surgery).

This project seeks to advance the state of the art in AI-based surgical tool detection and tracking by designing novel semi-supervised and weakly-supervised approaches able to achieve robust and real-time performance.

This project aims to develop and validate deep learning models to predict, from MRI and clinical data, which tumours are likely to grow and require treatment. Thestudent will be able to focus on designing novel radiomics analysis for vestibular schwannoma while exploiting an existing fully-automated AI tumour segmentation framework.

The CAI4CAI Research Group website is now live! Read about our research vision in our Proceedings of the IEEE paper.