Barts Life Sciences is recruiting for a digital fellow
Barts Life Sciences is recruiting for a digital fellow for our programme of AI and machine learning projects, CAP-AI.
This project, ThyrAI, will develop automatic interpretation of thyroid biopsy specimen slides for automated diagnosis using machine and deep learning approaches. Computer programs can then be devised for feature extraction from image data and feeding of this information to classifiers for automated labelling of thyroid nodules as benign or malignant. Such approaches, can prove to be more objective, fast and accurate. Thus, slide based computer-aided diagnosis (CAD) thyroid sample characterisation could potentially replace the need of performing unnecessary surgeries for nodules with uncertain conclusion. An open source classification algorithm will be of use across the NHS helping histopathologists deliver diagnosis with accuracy, especially supporting trainees and not yet experienced professionals. Computer vision can simulate human vision mechanism with the advantage of high speed and low cost. This project will allow us to generate an automated thyroid cancer detection and classification system for histopathology slides to achieve a high performance and accurate diagnosis. A key point in achieving equivalent or better accuracy, is the optimal choice of features to be extracted from the specimens. In addition, we aim to incorporate a model harnessing histopathology along with genetic data using machine learning to optimize thyroid cancer prediction and individualized diagnosis. Genetic predisposition for thyroid cancer, has a quite significant role and we aim to implement information regarding the genetic predisposition of an individual along with cytology data to reach highest accuracy on cancer diagnosis and decision making for the actual need of invasive procedures.
More information and how to apply can be found here: