Exploiting multi-task learning for endoscopic vision in robotic surgery

Aim of the PhD Project:
Learning appropriate surgical vision models with multi-task to perform better than learning each task independently
Learning appropriate surgical vision models with multi-task and limited supervision to perform close to learning with multi-task and adequate supervision
Advancing the state of the art in combining depth and optical flow estimation, surgical instrument detection and anatomy recognition, as well as surgical action recognition.
Evaluating and validating endoscopic vision-based learning paradigms
Project description:
Multi-task learning is common in deep learning: For similar tasks like detection and segmentation, or detection and counting, this has already been achieved given the supervision of one for the other. There exists clear evidence that adding one side task would help the improvement of the main task, yet it is unclear how much benefits both tasks can get in these combinations, especially if they are not strongly correlated. For this reason, multiple tasks are normally processed independently in the current fashion. Another reason lies in the scalability of learning multiple tasks together in terms of both network optimization and practical implementation. To tackle this, careful designs of the conjunction of multiple tasks are needed; novel methodologies of learning paradigms are also expected.

This project is placed in the endoscopic image processing domain. We aim to develop a machine learning model with general visual intelligence capacity in robotic surgery, which includes depth and optical flow estimation, surgical instrument detection and anatomy recognition, as well as surgical action recognition. Depth and optical flow estimation as well as anatomy recognition are key requirements to develop autonomous robotic control schemes that are cognizant of the surgical scene. Automatic detection and tracking of surgical instruments from laparoscopic surgery videos further plays an important role for providing advanced surgical assistance to the clinical team, given the uncertainties associated with surgical robots kinematic chains and the potential presence of tools not directly manipulated by the robot. Being able to know how many and where are the instruments finds its applications such as: placing informative overlays on the screen; performing augmented reality without occluding instruments; visual servoing; surgical task automation; etc. Surgical action recognition is also critical to advance autonomous robotic assistance during the procedure and for automated auditing purposes.

Strains on the healthcare system in the UK create an acute need for finding more effective, efficient, safe, and accurate non-invasive imaging solutions for clinical decision-making, both in terms of diagnosis and prognosis, and to reduce unnecessary treatment procedures and associated costs. Medical imaging is currently undergoing a step-change facilitated through the advent of artificial intelligence (AI) techniques, in particular deep learning and statistical machine learning, the development of targeted molecular imaging probes and novel "push-button" imaging techniques. There is also the availability of low-cost imaging solutions, creating unique opportunities to improve sensitivity and specificity of treatment options leading to better patient outcome, improved clinical workflow and healthcare economics. However, a skills gap exists between these disciplines which this CDT is aiming to fill.

Consistent with our vision for the CDT in Smart Medical Imaging to train the next generation of medical imaging scientists, we will engage with the key beneficiaries of the CDT: (1) PhD students & their supervisors; (2) patient groups & their carers; (3) clinicians & healthcare providers; (4) healthcare industries; and (5) the general public. We have identified the following areas of impact resulting from the operation of the CDT.

- Academic Impact: The proposed multidisciplinary training and skills development are designed to lead to an appreciation of clinical translation of technology and generating pathways to impact in the healthcare system. Impact will be measured in terms of our students' generation of knowledge, such as their research outputs, conference presentations, awards, software, patents, as well as successful career destinations to a wide range of sectors; as well as newly stimulated academic collaborations, and the positive effect these will have on their supervisors, their career progression and added value to their research group, and the universities as a whole in attracting new academic talent at all career levels.

- Economic Impact: Our students will have high employability in a wide range of sectors thanks to their broad interdisciplinary training, transferable skills sets and exposure to industry, international labs, and the hospital environment. Healthcare providers (e.g. the NHS) will gain access to new technologies that are more precise and cost-efficient, reducing patient treatment and monitoring costs. Relevant healthcare industries (from major companies to SMEs) will benefit and ultimately profit from collaborative research with high emphasis on clinical translation and validation, and from a unique cohort of newly skilled and multidisciplinary researchers who value and understand the role of industry in developing and applying novel imaging technologies to the entire patient pathway.

- Societal Impact: Patients and their professional carers will be the ultimate beneficiaries of the new imaging technologies created by our students, and by the emerging cohort of graduated medical imaging scientists and engineers who will have a strong emphasis on patient healthcare. This will have significant societal impact in terms of health and quality of life. Clinicians will benefit from new technologies aimed at enabling more robust, accurate, and precise diagnoses, treatment and follow-up monitoring. The general public will benefit from learning about new, cutting-edge medical imaging technology, and new talent will be drawn into STEM(M) professions as a consequence, further filling the current skills gap between healthcare provision and engineering.

We have developed detailed pathways to impact activities, coordinated by a dedicated Impact & Engagement Manager, that include impact training provision, translational activities with clinicians and patient groups, industry cooperation and entrepreneurship training, international collaboration and networks, and engagement with the General Public.