Posts

• Title: Exploiting multi-task learning for endoscopic vision in robotic surgery
• First supervisor: Miaojing Shi
• Second supervisor: Tom Vercauteren
• Clinical Supervisor: Asit Arora
• Start date: October 2022

Project summary

Multi-task learning is common in deep learning, where clear evidence shows that jointly learning correlated tasks can improve on individual performances. Notwithstanding, in reality, many tasks are processed independently. The reasons are manifold:

CAI4CAI will be presenting their work at MICCAI 2021, the 24th International Conference on Medical Image Computing and Computer Assisted Intervention, held from 27 September to 1 October 2021 as a virtual event.

Join us at the IEEE International Ultrasonics Symposium where CAI4CAI members will present their work.

Christian Baker will be presenting on “Real-Time Ultrasonic Tracking of an Intraoperative Needle Tip with Integrated Fibre-optic Hydrophone” as part of the Tissue Characterization & Real Time Imaging (AM) poster session.

Join us for the TRABIT conference (7-10 Sept 2021) with outstanding speakers and fun networking events. Registration is free but mandatory.

You can check all the videos made by the PhD students of TRABIT to present their research projects on youtube.

CAI4CAI members are contributing to guest editing the Medical Image Analysis Special Issue on Explainable and Generalizable Deep Learning Methods for Medical Image Computing. The final submission dealine is on December 1, 2021 but papers will be reviewed as they arrive.

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.

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. The CME fellowship will support her to carry out proof-of-concept studies, start critical new collaborations within and outside the centre. She hopes the award will act as a stepping stone to enable future long-term fellowship and grants, thus to establish an independent research programme.