AIMC Topic: Deep Learning

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Using Machine Learning and Structural Neuroimaging to Detect First Episode Psychosis: Reconsidering the Evidence.

Schizophrenia bulletin
Despite the high level of interest in the use of machine learning (ML) and neuroimaging to detect psychosis at the individual level, the reliability of the findings is unclear due to potential methodological issues that may have inflated the existing...

Agreement of two pre-trained deep-learning neural networks built with transfer learning with six pathologists on 6000 patches of prostate cancer from Gleason2019 Challenge.

Romanian journal of morphology and embryology = Revue roumaine de morphologie et embryologie
INTRODUCTION: While the visual inspection of histopathology images by expert pathologists remains the golden standard method for grading of prostate cancer the quest for developing automated algorithms for the job is set and deep-learning techniques ...

Artificial Intelligence and Machine Learning in Cardiovascular Imaging.

Methodist DeBakey cardiovascular journal
Cardiovascular disease is the leading cause of mortality in Western countries and leads to a spectrum of complications that can complicate patient management. The emergence of artificial intelligence (AI) has garnered significant interest in many ind...

An Automatic Classification of the Early Osteonecrosis of Femoral Head with Deep Learning.

Current medical imaging
BACKGROUND: Osteonecrosis of Femoral Head (ONFH) is a common complication in orthopaedics, wherein femoral structures are usually damaged due to the impairment or interruption of femoral head blood supply.

Contribution of neural networks in the diagnosis and treatment of cardiac arrhythmia.

Discovery medicine
Arrhythmia is a dangerous disease in which the heart rhythm varies and it may be very fast or very slow. Rapid heartbeats can lead to shortness of breath, chest pain, and sudden weakness, whereas slow heartbeats can lead to dizziness, problems with c...

Development of an Artificial Intelligence Model to Identify a Dental Implant from a Radiograph.

The International journal of oral & maxillofacial implants
PURPOSE: The objective of this study was to develop a deep convolutional neural network (CNN) that would identify the brand and model of a dental implant from a radiograph.

[Quantitative Analysis of Emphysema in Ultra-high-resolution CT by Using Deep Learning Reconstruction: Comparison with Hybrid Iterative Reconstruction].

Nihon Hoshasen Gijutsu Gakkai zasshi
PURPOSE: The noise generated in ultra-high-resolution computed tomography (U-HRCT) images affects the quantitative analysis of emphysema. In this study, we compared the physical properties of reconstructed images for hybrid iterative reconstruction (...

[Generation of the Pseudo CT Image Based on the Deep Learning Technique Aimed for the Attenuation Correction of the PET Image].

Nihon Hoshasen Gijutsu Gakkai zasshi
Computed tomography (CT) is used for the attenuation correction (AC) of [F-18] fluoro-deoxy-glucose positron emission tomography (PET) image. However, acquisition of a CT image for this purpose requires increasing the radiation dose of the patient. T...

[Volume Measurements of Post-transplanted Liver of Pediatric Recipients Using Workstations and Deep Learning].

Nihon Hoshasen Gijutsu Gakkai zasshi
PURPOSE: The purpose of this study was to propose a method for segmentation and volume measurement of graft liver and spleen of pediatric transplant recipients on digital imaging and communications in medicine (DICOM) -format images using U-Net and t...

[Supplementing a Web-based Exposure Estimation System with Deep Learning for Automatic Classification of CT Images to Increase the Efficiency of Effective Dose Estimation].

Nihon Hoshasen Gijutsu Gakkai zasshi
PURPOSE: Web-based exposure estimation systems are advantageous for estimating exposure doses for computed tomography (CT) scans. However, such systems depend on the imaging conditions of the slices, and a considerable amount of time and effort is ne...