AIMC Topic: Deep Learning

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Satellite images and machine learning can identify remote communities to facilitate access to health services.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Community health systems operating in remote areas require accurate information about where people live to efficiently provide services across large regions. We sought to determine whether a machine learning analyses of satellite imagery c...

State-of-the-Art Deep Learning in Cardiovascular Image Analysis.

JACC. Cardiovascular imaging
Cardiovascular imaging is going to change substantially in the next decade, fueled by the deep learning revolution. For medical professionals, it is important to keep track of these developments to ensure that deep learning can have meaningful impact...

Deep-Learning-Based Semantic Labeling for 2D Mammography and Comparison of Complexity for Machine Learning Tasks.

Journal of digital imaging
Machine learning has several potential uses in medical imaging for semantic labeling of images to improve radiologist workflow and to triage studies for review. The purpose of this study was to (1) develop deep convolutional neural networks (DCNNs) f...

Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges.

Journal of digital imaging
Deep learning-based image segmentation is by now firmly established as a robust tool in image segmentation. It has been widely used to separate homogeneous areas as the first and critical component of diagnosis and treatment pipeline. In this article...

The Classification of Renal Cancer in 3-Phase CT Images Using a Deep Learning Method.

Journal of digital imaging
In this research, we exploit an image-based deep learning framework to distinguish three major subtypes of renal cell carcinoma (clear cell, papillary, and chromophobe) using images acquired with computed tomography (CT). A biopsy-proven benchmarking...

RIL-Contour: a Medical Imaging Dataset Annotation Tool for and with Deep Learning.

Journal of digital imaging
Deep-learning algorithms typically fall within the domain of supervised artificial intelligence and are designed to "learn" from annotated data. Deep-learning models require large, diverse training datasets for optimal model convergence. The effort t...

Assessment of Critical Feeding Tube Malpositions on Radiographs Using Deep Learning.

Journal of digital imaging
Assess the efficacy of deep convolutional neural networks (DCNNs) in detection of critical enteric feeding tube malpositions on radiographs. 5475 de-identified HIPAA compliant frontal view chest and abdominal radiographs were obtained, consisting of ...

Effectiveness of Deep Learning Algorithms to Determine Laterality in Radiographs.

Journal of digital imaging
Develop a highly accurate deep learning model to reliably classify radiographs by laterality. Digital Imaging and Communications in Medicine (DICOM) data for nine body parts was extracted retrospectively. Laterality was determined directly if encoded...