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A Technical Review of Convolutional Neural Network-Based Mammographic Breast Cancer Diagnosis.

Computational and mathematical methods in medicine
This study reviews the technique of convolutional neural network (CNN) applied in a specific field of mammographic breast cancer diagnosis (MBCD). It aims to provide several clues on how to use CNN for related tasks. MBCD is a long-standing problem, ...

Real-time gastric polyp detection using convolutional neural networks.

PloS one
Computer-aided polyp detection in gastric gastroscopy has been the subject of research over the past few decades. However, despite significant advances, automatic polyp detection in real time is still an unsolved problem. In this paper, we report on ...

Kirsch's, and everyone's, bind: How to build models for the wild?

Cognitive processing
Alexandra Kirsch proposed a general formal model of decision making. She proposed it as a model both of human psychology and of artificial intelligence. As one might expect, and as Don Ross explicated, this is a challenging, albeit fascinating, posit...

Learning Where to See: A Novel Attention Model for Automated Immunohistochemical Scoring.

IEEE transactions on medical imaging
Estimating over-amplification of human epidermal growth factor receptor 2 (HER2) on invasive breast cancer is regarded as a significant predictive and prognostic marker. We propose a novel deep reinforcement learning (DRL)-based model that treats imm...

The best predictor of ischemic coronary stenosis: subtended myocardial volume, machine learning-based FFR, or high-risk plaque features?

European radiology
OBJECTIVES: The present study aimed to compare the diagnostic performance of a machine learning (ML)-based FFR algorithm, quantified subtended myocardial volume, and high-risk plaque features for predicting if a coronary stenosis is hemodynamically s...

Fast and fully-automated detection and segmentation of pulmonary nodules in thoracic CT scans using deep convolutional neural networks.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Deep learning techniques have been extensively used in computerized pulmonary nodule analysis in recent years. Many reported studies still utilized hybrid methods for diagnosis, in which convolutional neural networks (CNNs) are used only as one part ...

Predicting factors for survival of breast cancer patients using machine learning techniques.

BMC medical informatics and decision making
BACKGROUND: Breast cancer is one of the most common diseases in women worldwide. Many studies have been conducted to predict the survival indicators, however most of these analyses were predominantly performed using basic statistical methods. As an a...

Biochemical, machine learning and molecular approaches for the differential diagnosis of Mucopolysaccharidoses.

Molecular and cellular biochemistry
This study was aimed to construct classification and regression tree (CART) model of glycosaminoglycans (GAGs) for the differential diagnosis of Mucopolysaccharidoses (MPS). Two-dimensional electrophoresis and liquid chromatography-tandem mass spectr...