Prostate Cancer Diagnosis Using an Explainable Credibility Estimation Network Incorporating a Rejection Mechanism

Motivation: The need to improve prostate cancer diagnosis through advanced understanding of lesion characteristics and reducing false positives led to this research. Goal(s): To create a pioneering integrated system using deep learning, capable of accurately assessing the benignity or malignancy of prostate MRI images, whilst reducing labeling costs and enhancing the reliability of classifications. Approach: The approach involves training a convolutional network with multi-parametric MRI images, incorporating credibility analysis to provide visually interpretable prostate cancer prediction results and reject low-credibility predictions. Results: The results showed improved reliability and efficacy, with the model discarding low-credibility predictions, thus mitigating potential risks associated with prediction failures. Impact: This study equips clinical practitioners with the ability to comprehend the decision-making process of the CAD system and manage the output results through an intuitive display. This results enhance diagnostic accuracy, potentially impacting clinicians' decision-making and patient outcomes.