Multi-Stage Classification-Based Deep Learning for Gleason System Grading Using Histopathological Images.

In this work, we introduced an automated diagnostic system for Gleason system grading and grade groups (GG) classification using whole slide images (WSIs) of digitized prostate biopsy specimens (PBSs). Our system first classifies the Gleason pattern (GP) from PBSs and then identifies the Gleason score (GS) and GG. We developed a comprehensive DL-based approach to develop a grading pipeline system for the digitized PBSs and consider GP as a classification problem (not segmentation) compared to current research studies (deals with as a segmentation problem).

A Deep Learning Pipeline for Grade Groups Classification Using Digitized Prostate Biopsy Specimens.

Prostate cancer is a significant cause of morbidity and mortality in the USA. In this paper, we develop a computer-aided diagnostic (CAD) system for automated grade groups (GG) classification using digitized prostate biopsy specimens (PBSs). Our CAD system aims to firstly classify the Gleason pattern (GP), and then identifies the Gleason score (GS) and GG.

Artificial Intelligence Based Framework to Quantify the Cardiomyocyte Structural Integrity in Heart Slices.

Purpose: Drug induced cardiac toxicity is a disruption of the functionality of cardiomyocytes which is highly correlated to the organization of the subcellular structures. We can analyze cellular structures by utilizing microscopy imaging data. However, conventional image analysis methods might miss structural deteriorations that are difficult to perceive.

A multiparametric MRI-based CAD system for accurate diagnosis of bladder cancer staging.

Appropriate treatment of bladder cancer (BC) is widely based on accurate and early BC staging. In this paper, a multiparametric computer-aided diagnostic (MP-CAD) system is developed to differentiate between BC staging, especially T1 and T2 stages, using T2-weighted (T2W) magnetic resonance imaging (MRI) and diffusion-weighted (DW) MRI. Our framework starts with the segmentation of the bladder wall (BW) and localization of the whole BC volume (V) and its extent inside the wall (V).

EPICANCER-Cancer Patients Presenting to the Emergency Departments in France: A Prospective Nationwide Study.

Background: We aimed to estimate the prevalence of cancer patients who presented to Emergency Departments (EDs), report their chief complaint and identify the predictors of 30-day all-cause mortality.

Patients And Methods: we undertook a prospective, cross-sectional study during three consecutive days in 138 EDs and performed a logistic regression to identify the predictors of 30-day mortality in hospitalized patients.

Results: A total of 1380 cancer patients were included.