World-wide incidence rate of prostate cancer has progressively increased with time
especially with the increased proportion of elderly population. Early detection of prostate …

Since the advent of U-Net, fully convolutional deep neural networks and its many variants
have completely changed the modern landscape of deep-learning based medical image …

Abstract Background and Objective: Image-guided clinical diagnosis can be achieved by
automatically and accurately segmenting prostate and prostatic cancer in male pelvic …

Abstract Purpose Multiparametric MRI (mp-MRI) is a widely used tool for diagnosing and
staging prostate cancer. The purpose of this study was to evaluate whether transfer learning …

This paper combines improved GrowCut and Zernik feature extraction and ensemble
learning techniques such as KNN, SVM, and MLP algorithms to prostate cancer detection …

Prostate cancer is one of the deadest cancers among human beings. To better diagnose the
prostate cancer, prostate lesion segmentation becomes a very important work, but its …

We hypothesize that probabilistic voxel-level classification of anatomy and malignancy in
prostate MRI, although typically posed as near-identical segmentation tasks via U-Nets …

Background In middle-aged and older men, prostate cancer (PCa) is a common tumor
disease with a mortality rate second only to lung cancer. The automatic and accurate …

Automatic prostate tumor segmentation is often unable to identify the lesion even if multi-
parametric MRI data is used as input, and the segmentation output is difficult to verify due to …

Prostate cancer is a malignant tumor that occurs in the male prostate. Prostate cancer
lesions have the characteristics of small size and blurry outline, which is a challenge to …