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Implementation of model explainability for a basic brain tumor detection using convolutional neural networks on MRI slices.

Neuroradiology Jun 4, 2020

PURPOSE: While neural networks gain popularity in medical research, attempts to make the decisions of a model explainable are often only made towards the end of the development process once a high predictive accuracy has been achieved.

Humans Neural Networks, Computer Diagnosis, Differential Brain Neoplasms Glioblastoma Contrast Media Bayes Theorem Magnetic Resonance Imaging Image Interpretation, Computer-Assisted Datasets as Topic Neurilemmoma

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Deep learning-based reconstruction of ultrasound images from raw channel data.

International journal of computer assisted radiology and surgery Jun 3, 2020

PURPOSE: We investigate the feasibility of reconstructing ultrasound images directly from raw channel data using a deep learning network. Starting from the raw data, we present the network the full measurement information, allowing for a more generic...

Reference Values Signal-To-Noise Ratio Phantoms, Imaging Image Processing, Computer-Assisted Models, Theoretical Contrast Media Humans Algorithms Reproducibility of Results Diagnosis, Computer-Assisted Deep Learning Ultrasonography

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Machine learning paradigm for dynamic contrast-enhanced MRI evaluation of expanding bladder.

Frontiers in bioscience (Landmark edition) Jun 1, 2020

Delineation of the bladder under a dynamic contrast enhanced (DCE)-MRI protocol requires robust segmentation. However, this method is subject to errors due to variations in the content of fluid within the bladder, as well as presence of air and simil...

Humans Contrast Media Magnetic Resonance Imaging Urinary Bladder Radiographic Image Enhancement Neural Networks, Computer Machine Learning Algorithms Reproducibility of Results

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Fully automated intracranial aneurysm detection and segmentation from digital subtraction angiography series using an end-to-end spatiotemporal deep neural network.

Journal of neurointerventional surgery May 29, 2020

BACKGROUND: Intracranial aneurysms (IAs) are common in the population and may cause death.

Angiography, Digital Subtraction Humans Deep Learning Cerebral Angiography Neural Networks, Computer Time Factors Intracranial Aneurysm Retrospective Studies Contrast Media Female Aged Adult Male Middle Aged

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Motion artifacts reduction in brain MRI by means of a deep residual network with densely connected multi-resolution blocks (DRN-DCMB).

Magnetic resonance imaging May 16, 2020

OBJECTIVE: Magnetic resonance imaging (MRI) acquisition is inherently sensitive to motion, and motion artifact reduction is essential for improving image quality in MRI.

Movement Brain Deep Learning Contrast Media Magnetic Resonance Imaging Artifacts Image Enhancement Signal-To-Noise Ratio Humans

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Dosimetry-Driven Quality Measure of Brain Pseudo Computed Tomography Generated From Deep Learning for MRI-Only Radiation Therapy Treatment Planning.

International journal of radiation oncology, biology, physics May 14, 2020

PURPOSE: This study aims to evaluate the impact of key parameters on the pseudo computed tomography (pCT) quality generated from magnetic resonance imaging (MRI) with a 3-dimensional (3D) convolutional neural network.

Brain Retrospective Studies Tomography, X-Ray Computed Brain Neoplasms Humans Neural Networks, Computer Skull Radiometry Deep Learning Contrast Media Magnetic Resonance Imaging Radiotherapy

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Automating three-dimensional osteoarthritis histopathological grading of human osteochondral tissue using machine learning on contrast-enhanced micro-computed tomography.

Osteoarthritis and cartilage May 11, 2020

OBJECTIVE: To develop and validate a machine learning (ML) approach for automatic three-dimensional (3D) histopathological grading of osteochondral samples imaged with contrast-enhanced micro-computed tomography (CEμCT).

Cartilage, Articular Humans X-Ray Microtomography Machine Learning Arthroplasty, Replacement, Knee Severity of Illness Index Tibia Imaging, Three-Dimensional Femur Contrast Media Osteoarthritis, Knee

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Deep learning based classification of solid lipid-poor contrast enhancing renal masses using contrast enhanced CT.

The British journal of radiology May 11, 2020

OBJECTIVE: Establish a workflow that utilizes convolutional neural nets (CNN) to classify solid, lipid-poor, contrast enhancing renal masses using multiphase contrast enhanced CT (CECT) images and to assess the performance of the resulting network.

Humans Kidney Neoplasms Retrospective Studies Middle Aged Carcinoma, Renal Cell Tomography, X-Ray Computed Deep Learning Angiomyolipoma Female Contrast Media Adenoma, Oxyphilic Male

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Deep Convolutional Neural Networks-Based Automatic Breast Segmentation and Mass Detection in DCE-MRI.

Computational and mathematical methods in medicine May 5, 2020

Breast segmentation and mass detection in medical images are important for diagnosis and treatment follow-up. Automation of these challenging tasks can assist radiologists by reducing the high manual workload of breast cancer analysis. In this paper,...

Breast Neoplasms Female Middle Aged Humans Aged Computational Biology Contrast Media Young Adult Neural Networks, Computer Magnetic Resonance Imaging Adult Databases, Factual Breast Image Interpretation, Computer-Assisted

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Automated classification of solid renal masses on contrast-enhanced computed tomography images using convolutional neural network with decision fusion.

European radiology Apr 29, 2020

OBJECTIVES: To develop a deep learning-based method for automated classification of renal cell carcinoma (RCC) from benign solid renal masses using contrast-enhanced computed tomography (CECT) images.

Algorithms Angiomyolipoma Retrospective Studies Diagnosis, Differential Humans Adenoma, Oxyphilic Tomography, X-Ray Computed Image Interpretation, Computer-Assisted Neural Networks, Computer Contrast Media Kidney Neoplasms Machine Learning Carcinoma, Renal Cell

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AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Implementation of model explainability for a basic brain tumor detection using convolutional neural networks on MRI slices.

Deep learning-based reconstruction of ultrasound images from raw channel data.

Machine learning paradigm for dynamic contrast-enhanced MRI evaluation of expanding bladder.

Fully automated intracranial aneurysm detection and segmentation from digital subtraction angiography series using an end-to-end spatiotemporal deep neural network.

Motion artifacts reduction in brain MRI by means of a deep residual network with densely connected multi-resolution blocks (DRN-DCMB).

Dosimetry-Driven Quality Measure of Brain Pseudo Computed Tomography Generated From Deep Learning for MRI-Only Radiation Therapy Treatment Planning.

Automating three-dimensional osteoarthritis histopathological grading of human osteochondral tissue using machine learning on contrast-enhanced micro-computed tomography.

Deep learning based classification of solid lipid-poor contrast enhancing renal masses using contrast enhanced CT.

Deep Convolutional Neural Networks-Based Automatic Breast Segmentation and Mass Detection in DCE-MRI.

Automated classification of solid renal masses on contrast-enhanced computed tomography images using convolutional neural network with decision fusion.

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