PCa Detection

Prostate cancer (PCa) detection research focuses on developing accurate and efficient methods for identifying cancerous lesions, often using medical imaging data like MRI and PET scans. Current efforts involve adapting and improving existing machine learning techniques, such as principal component analysis (PCA) and its variants (e.g., robust PCA, online PCA), deep learning models (including transformers and autoencoders), and various dimensionality reduction methods to handle the high dimensionality and noise inherent in medical images. These advancements aim to improve diagnostic accuracy, reduce reliance on extensive manual annotation, and ultimately enhance patient care by enabling earlier and more precise PCa detection.