Radiomics for Gleason Score Detection through Deep Learning

Radiomics and Deep Learning

Melanoma detection with a deep learning model

Background: Skin cancer is one of the most common forms of cancer in the world and melanoma is the deadliest type of skin cancer. Both melanoma and melanocytic nevi begin in melanocytes (cells that produce melanin). However, melanocytic nevi are benign whereas melanoma is malignant. This work proposes a deep learning model for classification of these two lesions.    Methods: In this analytic s...

Gleason Score 7 Prostate Cancers Emerge through Branched Evolution of Clonal Gleason Pattern 3 and 4.

Purpose: The molecular features that account for the distinct histology and aggressive biological behavior of Gleason pattern 4 (Gp4) versus Gp3 prostate cancer, and whether Gp3 tumors progress directly to Gp4, remain to be established.Experimental Design: Whole-exome sequencing and transcriptome profiling of laser capture-microdissected adjacent Gp3 and cribiform Gp4 were used to determine the...

Concept drift detection in business process logs using deep learning

Process mining provides a bridge between process modeling and analysis on the one hand and data mining on the other hand. Process mining aims at discovering, monitoring, and improving real processes by extracting knowledge from event logs. However, as most business processes change over time (e.g. the effects of new legislation, seasonal effects and etc.), traditional process mining techniques ...

Discovery Radiomics for Computed Tomography Cancer Detection

Lung cancer is the leading cause for cancer related deaths. As such, there is an urgent need for a streamlined process that can allow radiologists to provide diagnosis with greater efficiency and accuracy. A powerful tool to do this is radiomics: a high-dimension imaging feature set. In this study, we take the idea of radiomics one step further by introducing the concept of discovery radiomics ...