  The diagnosis of prostate cancer faces a problem with overdiagnosis that
leads to damaging side effects due to unnecessary treatment. Research has shown
that the use of multi-parametric magnetic resonance images to conduct biopsies
can drastically help to mitigate the overdiagnosis, thus reducing the side
effects on healthy patients. This study aims to investigate the use of deep
learning techniques to explore computer-aid diagnosis based on MRI as input.
Several diagnosis problems ranging from classification of lesions as being
clinically significant or not to the detection and segmentation of lesions are
addressed with deep learning based approaches.
  This thesis tackled two main problems regarding the diagnosis of prostate
cancer. Firstly, XmasNet was used to conduct two large experiments on the
classification of lesions. Secondly, detection and segmentation experiments
were conducted, first on the prostate and afterward on the prostate cancer
lesions. The former experiments explored the lesions through a two-dimensional
space, while the latter explored models to work with three-dimensional inputs.
For this task, the 3D models explored were the 3D U-Net and a pretrained 3D
ResNet-18. A rigorous analysis of all these problems was conducted with a total
of two networks, two cropping techniques, two resampling techniques, two crop
sizes, five input sizes and data augmentations experimented for lesion
classification. While for segmentation two models, two input sizes and data
augmentations were experimented. However, while the binary classification of
the clinical significance of lesions and the detection and segmentation of the
prostate already achieve the desired results (0.870 AUC and 0.915 dice score
respectively), the classification of the PIRADS score and the segmentation of
lesions still have a large margin to improve (0.664 accuracy and 0.690 dice
score respectively).
