Interpretable Human-in-the-loop Modelling Solutions for Anterior Cruciate Ligament (ACL) Injury Classification
Even though many Machine Learning (ML) and Deep Learning (DL) object identification and classification methods have equalled or surpassed human level performance, they still face adoption challenges, especially in the health sector. Adoption of AI in medical image analysis highly depends on trust users have in an automated system. Making AI transparent is one way to increase its adoption. Typical accuracy of a radiologist interpreting ligament injury from an MRI is 94%. ML and DL based classifiers can achieve similar levels of accuracy, but their black box nature means that we do not know if they are diagnosing using relevant features. Studies in Longoni et al. have shown that consumers/patients in healthcare are concerned that ML and DL based solutions would not account for their unique injury features as much as humans would do. This can be true for DL models used in medical image classification, which are trained on large datasets, and may not be able to look for unique features in a patient’s imaging output. This suggests that interpretability, human-In-the-loop (HITL) feature labelling, and unlearning inappropriate features could be combined to move towards more personalised models.
The main objective of this project is to develop transparent medical image analysis solutions where domain experts can participate in the model building process by combining model interpretability and HITL ML techniques. These solutions will be driven by and demonstrated in the important application of Anterior Cruciate Ligament (ACL) injury classification. Models with prototype layers will be used to develop interpretable classifiers. The proposed project will involve designing user interfaces that help users navigate through a trained model’s decision process, and enable them to interact with the model by reporting back inaccurate image features the model may have picked up in its training phase. This will be followed by unlearning incorrect features that the trained model may be using to reach classification decisions, which in turn improves classification performance. Methods developed will be released to radiologists for evaluation. The resulting interpretability and unlearning solutions should be transferable across different knee joint injury classification problems, as well as to other body parts, and imaging modalities. This project will have three main contributions: (1) Prototype layer based medical images classification and feature visualization; (2) A user interface for HITL feature labelling feedback; (3) Improved system performance through unlearning incorrect features extracted by a trained model.