Domain Transfer Through Image-to-Image Translation for Uncertainty-Aware
  Prostate Cancer Classification
Meng Zhou Amoon Jamzad Jason Izard Alexandre Menard Robert Siemens Parvin Mousavi
Prostate Cancer (PCa) is often diagnosed using High-resolution 3.0 Tesla(T)
MRI, which has been widely established in clinics. However, there are still
many medical centers that use 1.5T MRI units in the actual diagnostic process
of PCa. In the past few years, deep learning-based models have been proven to
be efficient on the PCa classification task and can be successfully used to
support radiologists during the diagnostic process. However, training such
models often requires a vast amount of data, and sometimes it is unobtainable
in practice. Additionally, multi-source MRIs can pose challenges due to
cross-domain distribution differences. In this paper, we have presented a novel
approach for unpaired image-to-image translation of prostate mp-MRI for
classifying clinically significant PCa, to be applied in data-constrained
settings. First, we introduce domain transfer, a novel pipeline to translate
unpaired 3.0T multi-parametric prostate MRIs to 1.5T, to increase the number of
training data. Second, we estimate the uncertainty of our models through an
evidential deep learning approach; and leverage the dataset filtering technique
during the training process. Furthermore, we introduce a simple, yet efficient
Evidential Focal Loss that incorporates the focal loss with evidential
uncertainty to train our model. Our experiments demonstrate that the proposed
method significantly improves the Area Under ROC Curve (AUC) by over 20%
compared to the previous work (98.4% vs. 76.2%). We envision that providing
prediction uncertainty to radiologists may help them focus more on uncertain
cases and thus expedite the diagnostic process effectively. Our code is
available at https://github.com/med-i-lab/DT_UE_PCa