Active and continual learning strategies for deep learning assisted interactive segmentation of new databases

Lead Research Organisation: King's College London
Department Name: Imaging & Biomedical Engineering

Abstract

Aim of the PhD Porject:

Develop interactive deep learning approaches to continually segment databases of images for which no previously annotated training databases exist
Design active learning strategies to retrieve on-the-fly, cases whose manual segmentation will be the most informative for continual learning
Create annotation tools that support accelerated adoption of AI for new applications.
Project Description / Background:

Contemporary progresses in machine learning and artificial intelligence have permitted the development of tools that can assist clinicians in exploiting and quantifying clinical data including images, textual reports and genetic information. State-of-the-art algorithms are becoming mature enough to provide automated analysis when provided with enough high-quality training data and when applied to well-controlled clinical studies and trials [1], [2]. It is clear though that producing manual voxel-accurate medical image segmentation labels is tedious, time-consuming and costly as it usually requires profound radiological expertise. Data annotation is often a rate-limiting factor for the development of application-specific deep learning based image segmentation solutions.

In this project, we will focus on designing machine learning approaches to assist and accelerate the manual segmentation of structures of interest across a database, potentially starting from scratch. Adapting deep learning to support new applications while reducing the burden required to collect and annotate datasets for training purposes remains an active research area [7]. This topic shares challenges with domain adaption [3], for example when trying to limit the amount of new annotations required when new generations of scanners are being rolled out. Naively applying a pre-trained model to an imaging source that may slightly differ from the one used to acquire the training data set on which the model was trained indeed often results in dramatic failures. New annotations are often required to confidently bridge the domain gap and validate the performance of domain adaptation techniques.

In such cases, clinicians are typically left with fully manual or generic interactive methods to delineate structures of interest. Interactive deep learning methodologies are emerging to combine rich prior knowledge embedded in retrospective data from previous patients with as-sparse-as-possible annotations provided by clinicians [4], [5]. Yet, these techniques do currently not continue to learn and improve when being used for new cases. Concurrently, algorithms have been designed to exploit weak labels annotated across a data set to train deep neural networks [6]. Again, these methods require manually segmented ground truth for validation purposes and do not learn for being presented with new cases.

This project will consider the problem of gradually annotating an image segmentation dataset with potentially only very little prior knowledge. This is a timely research question which very few machine learning works have considered so far.

Publications

10 25 50

Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S022104/1 01/10/2019 31/03/2028
2442179 Studentship EP/S022104/1 01/10/2020 30/09/2024 Theodore David Barfoot