Segmenting Prostate Cancer on TRUS Images with a Small Dataset: A Comprehensive Methodology

Abstract

The use of mathematical algorithms for disease identification has gained traction in recent years and has paved the way for the creation of novel tools that can swiftly and accurately detect pathologies. In particular, modern machine learning techniques have garnered significant attention in this domain and are currently among the most widely used algorithms. Despite their popularity, the implementation and training of these neural networks can be daunting, owing to the intricate nature of the data and the complexity of the training process. To address these challenges, this paper suggests an efficient neural network training algorithm that employs iterative analysis and gradient computation for each data packet, thus ensuring the attainment of optimal quality metrics.