Оброблення зображень КТ печінки за допомогою згорткової нейронної мережі

The methods of learning Artificial Neural Network to analyze computed tomographyimages and identify them on the diseaseРассмотрены методы обучения модели машинного обучения для анализа изображений компьютернойтомографии и выявления на них заболевания пациента с циррозом печениРозглянуто методи навчання нейронної мережі для аналізу зображень комп’ютерної томографії печінки та виявлення на них захворювання пацієнта на цироз печінк

A Comparative Study for 2D and 3D Computer-aided Diagnosis Methods for Solitary Pulmonary Nodules

Many computer-aided diagnosis (CAD) methods, including 2D and 3D approaches, have been proposed for solitary pulmonary nodules (SPNs). However, the detection and diagnosis of SPNs remain challenging in many clinical circumstances. One goal of this work is to investigate the relative diagnostic accuracy of 2D and 3D methods. An additional goal is to develop a two-stage approach that combines the simplicity of 2D and the accuracy of 3D methods. The experimental results show statistically significant differences between the diagnostic accuracy of 2D and 3D methods. The results also show that with a very minor drop in diagnostic performance the two-stage approach can significantly reduce the number of nodules needed to be processed by the 3D method, streamlining the computational demand

Beyond the randomized clinical trial: innovative data science to close the pediatric evidence gap

Despite the application of advanced statistical and pharmacometric approaches to pediatric trial data, a large pediatric evidence gap still remains. Here, we discuss how to collect more data from children by using real-world data from electronic health records, mobile applications, wearables, and social media. The large datasets collected with these approaches enable, and may demand, the use of artificial intelligence and machine learning to allow the data to be analyzed for decision-making. Applications of this approach are presented, which include the prediction of future clinical complications, medical image analysis, identification of new pediatric endpoints and biomarkers, the prediction of treatment non-responders and the prediction of placebo-responders for trial enrichment. Finally, we discuss how to bring machine learning from science to pediatric clinical practice. We conclude that advantage should be taken of the current opportunities offered by innovations in data science and machine learning to close the pediatric evidence gap.Pharmacolog

Computerâ aided diagnosis of pulmonary nodules on CT scans: Segmentation and classification using 3D active contours

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135040/1/mp7129.pd

Image analysis for classification of dysplasia in Barrett’s esophagus using endoscopic optical coherence tomography

Barrett’s esophagus (BE) and associated adenocarcinoma have emerged as a major health care problem. Endoscopic optical coherence tomography is a microscopic sub-surface imaging technology that has been shown to differentiate tissue layers of the gastrointestinal wall and identify dysplasia in the mucosa, and is proposed as a surveillance tool to aid in management of BE. In this work a computer-aided diagnosis (CAD) system has been demonstrated for classification of dysplasia in Barrett’s esophagus using EOCT. The system is composed of four modules: region of interest segmentation, dysplasia-related image feature extraction, feature selection, and site classification and validation. Multiple feature extraction and classification methods were evaluated and the process of developing the CAD system is described in detail. Use of multiple EOCT images to classify a single site was also investigated. A total of 96 EOCT image-biopsy pairs (63 non-dysplastic, 26 low-grade and 7 high-grade dysplastic biopsy sites) from a previously described clinical study were analyzed using the CAD system, yielding an accuracy of 84% for classification of non-dysplastic vs. dysplastic BE tissue. The results motivate continued development of CAD to potentially enable EOCT surveillance of large surface areas of Barrett’s mucosa to identify dysplasia