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    Evaluating Uncertainty-Based Deep Learning Explanations for Prostate Lesion Detection
    (Jmlr-Journal Machine Learning Research, 2022) Trombley, Christopher M.; Gulum, Mehmet Akif; Ozen, Merve; Esen, Enes; Aksamoglu, Melih; Kantardzic, Mehmed
    Deep learning has demonstrated impressive accuracy for prostate lesion identification and classification. Deep learning algorithms are considered black-box methods therefore they require explanation methods to gain insight into the model's classification. For high stakes tasks such as medical diagnosis, it is important that explanation methods are able to estimate explanation uncertainty. Recently, there have been various methods proposed for providing uncertainty-based explanations. However, the clinical effectiveness of uncertaintybased explanation methods and what radiologists deem explainable within this context is still largely unknown. To that end, this pilot study investigates the effectiveness of uncertainty-based prostate lesion detection explanations. It also attempts to gain insight into what radiologists consider explainable. An experiment was conducted with a cohort of radiologists to determine if uncertainty-based explanation methods improve prostate lesion detection. Additionally, a qualitative assessment of each method was conducted to gain insight into what characteristics make an explanation method suitable for radiology end use. It was found that uncertainty-based explanation methods increase lesion detection performance by up to 20%. It was also found that perceived explanation quality is related to actual explanation quality. This pilot study demonstrates the potential use of explanation methods for radiology end use and gleans insight into what radiologists deem explainable.