Classification of Prostate Cancer in 3D Magnetic Resonance Imaging Data
  based on Convolutional Neural Networks
Malte Rippa Ruben Schulze Marian Himstedt Felice Burn
Prostate cancer is a commonly diagnosed cancerous disease among men
world-wide. Even with modern technology such as multi-parametric magnetic
resonance tomography and guided biopsies, the process for diagnosing prostate
cancer remains time consuming and requires highly trained professionals. In
this paper, different convolutional neural networks (CNN) are evaluated on
their abilities to reliably classify whether an MRI sequence contains malignant
lesions. Implementations of a ResNet, a ConvNet and a ConvNeXt for 3D image
data are trained and evaluated. The models are trained using different data
augmentation techniques, learning rates, and optimizers. The data is taken from
a private dataset, provided by Cantonal Hospital Aarau. The best result was
achieved by a ResNet3D, yielding an average precision score of 0.4583 and AUC
ROC score of 0.6214.