|dc.description.abstract||Prostate Cancer (PCa) is a disease characterized by uncontrolled growth of cells
within the prostate gland located in the male reproductive system. In 2020 1.4
million new cases and 375 thousand deaths worldwide from PCa were estimated.
It is the fourth most commonly diagnosed cancer among both genders globally.
Overall, it’s the second most commonly diagnosed cancer and fifth leading cause
of cancer death in men.
Artificial Intelligence (AI) is a rapid growing domain with a promising potential for healthcare professionals to draw insights from data, and already Machine
Learning (ML) are used in discovery and development of medicines, diagnosis,
medical imaging and to train and aid the experts judgement. By using Deep
Learning (DL) to discover concealed links between Multi-Parametric Magnetic
Resonance Imaging (mp-MRI) and PCa to generate a ground truth can potentially strengthen the certainty of the PCa diagnosis by reducing the chance for
misdiagnosis due to inter-rater reliability. ML algorithms can assist with analysis of medical data, with the effect being a reduced workload of the skilled workforce. However, many AI algorithms and particularly DL often benefits from large
amount of annotated data which can be problematic to acquire in the medical domain due to various reasons such as permission, transparency and privacy. SelfSuperviced Learning (SSL) in computer vision involve performing two tasks, by
firstly pretraining a model with unlabeled data with the aim of using the learned
latent representation for a different task. Transfer Learning (TL) could be capable
to tackle the issue of data scarcity commonly found in the medical domain.
This thesis explores the potential of improving the diagnostic pathway of prostate
cancer with a multi-parametric generatvie self-supervised approach, by studying
whether mp-MRI is effective for classifying clinical significance of PCa. The proposed method uses three-dimensional Apparent Diffusion Coefficient (ADC) and
T2-Weighted (T2w) in multi-parametric models with U-shaped Convolutional
Neural Network (U-net) architecture and Visual Geometry Group (VGG)16 backbone to explore whether utilizing generative Self-Superviced (SS) approach is
useful to classify with a small amount of data compared to a (Fully) Superviced
(FS) approach. In this thesis anisotropic are combined with isotropic features in
the latent space of the U-net bottleneck.
The results from three dimensional reconstruction of masked mp-MRI with generative self-supervised learning showed good results. However, the effectiveness of the proposed methodology were seen to struggle with transferring valuable learned similarity between the source domain and target domain. The final
evaluation of the self-supervised approach and fully supervised approach did not
yield good enough results quantify to support this aim of the thesis. The multiparametric models were seen to behave more stable in data scarce settings compared to models where a single Magnetic Resonance Imaging (MRI) sequence