  Objective: Radiomics-driven Computer Aided Diagnosis (CAD) has shown
considerable promise in recent years as a potential tool for improving clinical
decision support in medical oncology, particularly those based around the
concept of Discovery Radiomics, where radiomic sequencers are discovered
through the analysis of medical imaging data. One of the main limitations with
current CAD approaches is that it is very difficult to gain insight or
rationale as to how decisions are made, thus limiting their utility to
clinicians. Methods: In this study, we propose CLEAR-DR, a novel interpretable
CAD system based on the notion of CLass-Enhanced Attentive Response Discovery
Radiomics for the purpose of clinical decision support for diabetic
retinopathy. Results: In addition to disease grading via the discovered deep
radiomic sequencer, the CLEAR-DR system also produces a visual interpretation
of the decision-making process to provide better insight and understanding into
the decision-making process of the system. Conclusion: We demonstrate the
effectiveness and utility of the proposed CLEAR-DR system of enhancing the
interpretability of diagnostic grading results for the application of diabetic
retinopathy grading. Significance: CLEAR-DR can act as a potential powerful
tool to address the uninterpretability issue of current CAD systems, thus
improving their utility to clinicians.
