AIMC Topic: Tomography, X-Ray Computed

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Research of Multimodal Medical Image Fusion Based on Parameter-Adaptive Pulse-Coupled Neural Network and Convolutional Sparse Representation.

Computational and mathematical methods in medicine Jan 24, 2020
Visual effects of medical image have a great impact on clinical assistant diagnosis. At present, medical image fusion has become a powerful means of clinical application. The traditional medical image fusion methods have the problem of poor fusion re...
Positron-Emission Tomography Image Interpretation, Computer-Assisted Tomography, Emission-Computed, Single-Photon Computational Biology Magnetic Resonance Imaging Multimodal Imaging Brain Humans Tomography, X-Ray Computed Neoplasms Neural Networks, Computer Algorithms Databases, Factual
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Deep Learning Features Improve the Performance of a Radiomics Signature for Predicting KRAS Status in Patients with Colorectal Cancer.

Academic radiology Jan 23, 2020
RATIONALE AND OBJECTIVES: We assess the performance of a model combining a deep convolutional neural network and a hand-crafted radiomics signature for predicting KRAS status in patients with colorectal cancer (CRC).
Colorectal Neoplasms Deep Learning Proto-Oncogene Proteins p21(ras) Humans Algorithms Tomography, X-Ray Computed
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Application of Deep Learning Convolutional Neural Networks for Internal Tablet Defect Detection: High Accuracy, Throughput, and Adaptability.

Journal of pharmaceutical sciences Jan 23, 2020
Tablet defects encountered during the manufacturing of oral formulations can result in quality concerns, timeline delays, and elevated financial costs. Internal tablet cracking is not typically measured in routine inspections but can lead to batch fa...
Neural Networks, Computer Image Processing, Computer-Assisted Tablets Tomography, X-Ray Computed Deep Learning
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Automated body composition analysis of clinically acquired computed tomography scans using neural networks.

Clinical nutrition (Edinburgh, Scotland) Jan 22, 2020
BACKGROUND & AIMS: The quantity and quality of skeletal muscle and adipose tissue is an important prognostic factor for clinical outcomes across several illnesses. Clinically acquired computed tomography (CT) scans are commonly used for quantificatio...
Adipose Tissue Predictive Value of Tests Body Composition Radiographic Image Interpretation, Computer-Assisted Lumbar Vertebrae Adiposity Europe Sarcopenia Muscle, Skeletal Female Male Automation Humans Aged Reproducibility of Results Neural Networks, Computer North America Middle Aged Adult Deep Learning Tomography, X-Ray Computed
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A deep convolutional neural network architecture for interstitial lung disease pattern classification.

Medical & biological engineering & computing Jan 22, 2020
Interstitial lung disease (ILD) refers to a group of various abnormal inflammations of lung tissues and early diagnosis of these disease patterns is crucial for the treatment. Yet it is difficult to make an accurate diagnosis due to the similarity am...
Neural Networks, Computer Databases, Factual Tomography, X-Ray Computed Diagnosis, Computer-Assisted Humans Deep Learning Lung Diseases, Interstitial
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Image Quality and Lesion Detection on Deep Learning Reconstruction and Iterative Reconstruction of Submillisievert Chest and Abdominal CT.

AJR. American journal of roentgenology Jan 22, 2020
The objective of this study was to compare image quality and clinically significant lesion detection on deep learning reconstruction (DLR) and iterative reconstruction (IR) images of submillisievert chest and abdominopelvic CT. Our prospective mult...
Contrast Media Radiographic Image Interpretation, Computer-Assisted Radiation Dosage Middle Aged Deep Learning Imaging, Three-Dimensional Radiography, Thoracic Radiography, Abdominal Humans Prospective Studies Tomography, X-Ray Computed Female Aged Male
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SACNN: Self-Attention Convolutional Neural Network for Low-Dose CT Denoising With Self-Supervised Perceptual Loss Network.

IEEE transactions on medical imaging Jan 21, 2020
Computed tomography (CT) is a widely used screening and diagnostic tool that allows clinicians to obtain a high-resolution, volumetric image of internal structures in a non-invasive manner. Increasingly, efforts have been made to improve the image qu...
Attention Signal-To-Noise Ratio Neural Networks, Computer Tomography, X-Ray Computed Humans Image Processing, Computer-Assisted
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[Trends of development in CT and MRI doagnostics].

Magyar onkologia Jan 20, 2020
Modern imaging processes, with CT and MRI diagnostics among them, play an increasingly important role in the early diagnosis and determination of the extent and nature of illnesses and the evaluation of their response to therapy. The authors review t...
Tomography, X-Ray Computed Humans Magnetic Resonance Imaging Artificial Intelligence Software
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Dual-energy CT-based deep learning radiomics can improve lymph node metastasis risk prediction for gastric cancer.

European radiology Jan 17, 2020
OBJECTIVES: To build a dual-energy CT (DECT)-based deep learning radiomics nomogram for lymph node metastasis (LNM) prediction in gastric cancer.
Adenocarcinoma Lymphatic Metastasis Neoplasm Staging Deep Learning Lymph Nodes Nomograms Female Aged Aged, 80 and over Male Middle Aged Prognosis Adult Humans Retrospective Studies Stomach Neoplasms Tomography, X-Ray Computed
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Automatic lesion segmentation and classification of hepatic echinococcosis using a multiscale-feature convolutional neural network.

Medical & biological engineering & computing Jan 16, 2020
Hepatic echinococcosis (HE) is a life-threatening liver disease caused by parasites that requires a precise diagnosis and proper treatments. To assess HE lesions accurately, we propose a novel automatic HE lesion segmentation and classification netwo...
Tomography, X-Ray Computed Image Processing, Computer-Assisted Humans Automation Neural Networks, Computer Algorithms Echinococcosis, Hepatic
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