AIMC Topic: Diagnosis, Differential
Clear Filters Showing 331 to 340 of 753 articles
Deep learning-based transformation of H&E stained tissues into special stains.
Nature communications
Aug 12, 2021
Pathology is practiced by visual inspection of histochemically stained tissue slides. While the hematoxylin and eosin (H&E) stain is most commonly used, special stains can provide additional contrast to different tissue components. Here, we demonstra...
MR-Based Radiomics for Differential Diagnosis between Cystic Pituitary Adenoma and Rathke Cleft Cyst.
Computational and mathematical methods in medicine
Aug 10, 2021
BACKGROUND: It is often tricky to differentiate cystic pituitary adenoma from Rathke cleft cyst with visual inspection because of similar MRI presentations between them. We aimed to design an MR-based radiomics model for improving differential diagno...
Central Nervous System Cysts
Diagnosis, Computer-Assisted
Female
Humans
Adenoma
Adult
Neural Networks, Computer
Diagnosis, Differential
Computational Biology
Male
Machine Learning
Image Interpretation, Computer-Assisted
Magnetic Resonance Imaging
Middle Aged
Retrospective Studies
Semantics
Pituitary Neoplasms
Explainable DCNN based chest X-ray image analysis and classification for COVID-19 pneumonia detection.
Scientific reports
Aug 9, 2021
To speed up the discovery of COVID-19 disease mechanisms by X-ray images, this research developed a new diagnosis platform using a deep convolutional neural network (DCNN) that is able to assist radiologists with diagnosis by distinguishing COVID-19 ...
RCoNet: Deformable Mutual Information Maximization and High-Order Uncertainty-Aware Learning for Robust COVID-19 Detection.
IEEE transactions on neural networks and learning systems
Aug 3, 2021
The novel 2019 Coronavirus (COVID-19) infection has spread worldwide and is currently a major healthcare challenge around the world. Chest computed tomography (CT) and X-ray images have been well recognized to be two effective techniques for clinical...
Classification of glioblastoma versus primary central nervous system lymphoma using convolutional neural networks.
Scientific reports
Jul 26, 2021
A subset of primary central nervous system lymphomas (PCNSL) are difficult to distinguish from glioblastoma multiforme (GBM) on magnetic resonance imaging (MRI). We developed a convolutional neural network (CNN) to distinguish these tumors on contras...
Preliminary study on the application of renal ultrasonography radiomics in the classification of glomerulopathy.
BMC medical imaging
Jul 23, 2021
BACKGROUND: The aim of this study was to investigate the potential use of renal ultrasonography radiomics features in the histologic classification of glomerulopathy.
Detecting neonatal acute bilirubin encephalopathy based on T1-weighted MRI images and learning-based approaches.
BMC medical imaging
Jun 22, 2021
BACKGROUND: Neonatal hyperbilirubinemia is a common clinical condition that requires medical attention in newborns, which may develop into acute bilirubin encephalopathy with a significant risk of long-term neurological deficits. The current clinical...
Deep learning-based framework for the distinction of membranous nephropathy: a new approach through hyperspectral imagery.
BMC nephrology
Jun 19, 2021
BACKGROUND: Common subtypes seen in Chinese patients with membranous nephropathy (MN) include idiopathic membranous nephropathy (IMN) and hepatitis B virus-related membranous nephropathy (HBV-MN). However, the morphologic differences are not visible ...
A deep-learning based artificial intelligence (AI) approach for differentiation of clear cell renal cell carcinoma from oncocytoma on multi-phasic MRI.
Clinical imaging
Jun 18, 2021
PURPOSE: To investigate the diagnostic performance of a deep convolutional neural network for differentiation of clear cell renal cell carcinoma (ccRCC) from renal oncocytoma.
Distinguishing nontuberculous mycobacteria from Mycobacterium tuberculosis lung disease from CT images using a deep learning framework.
European journal of nuclear medicine and molecular imaging
Jun 16, 2021
PURPOSE: To develop and evaluate the effectiveness of a deep learning framework (3D-ResNet) based on CT images to distinguish nontuberculous mycobacterium lung disease (NTM-LD) from Mycobacterium tuberculosis lung disease (MTB-LD).
Stay Ahead of Medical AI
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.
Subscribe NowPopular Topics
- Machine Learning 31088
- Neural Networks, Computer 29743
- Algorithms 26874
- Deep Learning 26387
- Artificial Intelligence 24329
- Robotics 13315
- Image Processing, Computer-Assisted 9536
- Retrospective Studies 9125
- Magnetic Resonance Imaging 6192
- Reproducibility of Results 5600
- Tomography, X-Ray Computed 4772
- Support Vector Machine 4692
- Computational Biology 4243
- Brain 4053
- Computer Simulation 3885
Recent Journals
- Scientific reports 6015
- Sensors (Basel, Switzerland) 4794
- PloS one 4029
- Neural networks : the official journal of the International Neural Network Society 2930
- Computational intelligence and neuroscience 1931
- Computers in biology and medicine 1864
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 1686
- Studies in health technology and informatics 1278
- IEEE journal of biomedical and health informatics 1133
- Journal of chemical information and modeling 1003
- Nature communications 952
- Computer methods and programs in biomedicine 883
- Bioinformatics (Oxford, England) 862
- Briefings in bioinformatics 853
- IEEE transactions on neural networks and learning systems 828