  Prostate cancer is a very common disease among adult men. One in seven
Canadian men is diagnosed with this cancer in their lifetime. Super-Resolution
(SR) can facilitate early diagnosis and potentially save many lives. In this
paper, a robust and accurate model is proposed for prostate MRI SR. The model
is trained on the Prostate-Diagnosis and PROSTATEx datasets. The proposed model
outperformed the state-of-the-art prostate SR model in all similarity metrics
with notable margins. A new task-specific similarity assessment is introduced
as well. A classifier is trained for severe cancer detection and the drop in
the accuracy of this model when dealing with super-resolved images is used for
evaluating the ability of medical detail reconstruction of the SR models. The
proposed SR model is a step towards an efficient and accurate general medical
SR platform.
