AIMC Topic: Radiographic Image Enhancement

Clear Filters Showing 41 to 50 of 102 articles

DBT Masses Automatic Segmentation Using U-Net Neural Networks.

Computational and mathematical methods in medicine Jan 28, 2020
To improve the automatic segmentation accuracy of breast masses in digital breast tomosynthesis (DBT) images, we propose a DBT mass automatic segmentation algorithm by using a U-Net architecture. Firstly, to suppress the background tissue noise and e...
Humans Neural Networks, Computer Algorithms Mammography Radiographic Image Interpretation, Computer-Assisted Breast Radiographic Image Enhancement Female Sensitivity and Specificity Breast Neoplasms Computational Biology
View on PubMed DOI

Deep learning-based virtual noncontrast CT for volumetric modulated arc therapy planning: Comparison with a dual-energy CT-based approach.

Medical physics Dec 3, 2019
PURPOSE: The aim of this study was to develop a deep learning (DL) method for generating virtual noncontrast (VNC) computed tomography (CT) images from contrast-enhanced (CE) CT images (VNC ) and to evaluate its performance in dose calculations for h...
Radiography, Dual-Energy Scanned Projection Contrast Media Head Radiotherapy, Intensity-Modulated Radiographic Image Enhancement Humans Tomography, X-Ray Computed Reproducibility of Results Deep Learning Iodine Time Factors Neck
View on PubMed DOI

Machine-learning analysis of contrast-enhanced CT radiomics predicts recurrence of hepatocellular carcinoma after resection: A multi-institutional study.

EBioMedicine Nov 15, 2019
BACKGROUND: Current guidelines recommend surgical resection as the first-line option for patients with solitary hepatocellular carcinoma (HCC); unfortunately, postoperative recurrence rate remains high and there is no reliable prediction tool. We exp...
Hepatectomy Proportional Hazards Models Radiographic Image Enhancement Image Processing, Computer-Assisted Contrast Media Workflow Neoplasm Recurrence, Local Algorithms Prognosis Retrospective Studies Tomography, X-Ray Computed Female Male Liver Neoplasms Carcinoma, Hepatocellular Humans Machine Learning
View on PubMed DOI

Digital breast tomosynthesis versus digital mammography: integration of image modalities enhances deep learning-based breast mass classification.

European radiology Nov 5, 2019
OBJECTIVE: To evaluate the impact of utilizing digital breast tomosynthesis (DBT) or/and full-field digital mammography (FFDM), and different transfer learning strategies on deep convolutional neural network (DCNN)-based mass classification for breas...
Humans Aged Aged, 80 and over Young Adult Adult Neural Networks, Computer Machine Learning Retrospective Studies Female Radiographic Image Enhancement Mammography Diagnosis, Differential Radiographic Image Interpretation, Computer-Assisted Breast Breast Neoplasms Middle Aged Deep Learning Imaging, Three-Dimensional
View on PubMed DOI

Artificial Intelligence for Mammography and Digital Breast Tomosynthesis: Current Concepts and Future Perspectives.

Radiology Sep 24, 2019
Although computer-aided diagnosis (CAD) is widely used in mammography, conventional CAD programs that use prompts to indicate potential cancers on the mammograms have not led to an improvement in diagnostic accuracy. Because of the advances in machin...
Diagnosis, Computer-Assisted Algorithms Deep Learning Humans Female Mammography Breast Neoplasms Artificial Intelligence Radiographic Image Enhancement Risk Assessment Machine Learning
View on PubMed DOI

Metal artifact reduction for the segmentation of the intra cochlear anatomy in CT images of the ear with 3D-conditional GANs.

Medical image analysis Sep 4, 2019
Cochlear implants (CIs) are surgically implanted neural prosthetic devices that are used to treat severe-to-profound hearing loss. These devices are programmed post implantation and precise knowledge of the implant position with respect to the intra ...
Radiographic Image Enhancement Cochlea Metals Imaging, Three-Dimensional Artifacts Cochlear Implants Humans Neural Networks, Computer Algorithms Tomography, X-Ray Computed
View on PubMed DOI

A Hybridized ELM for Automatic Micro Calcification Detection in Mammogram Images Based on Multi-Scale Features.

Journal of medical systems May 15, 2019
Detection of masses and micro calcifications are a stimulating task for radiologists in digital mammogram images. Radiologists using Computer Aided Detection (CAD) frameworks to find the breast lesion. Micro calcification may be the early sign of bre...
Radiographic Image Enhancement Humans Female Machine Learning Diagnosis, Computer-Assisted Algorithms Calcinosis Mammography
View on PubMed DOI

Convolutional Neural Networks for the Segmentation of Microcalcification in Mammography Imaging.

Journal of healthcare engineering Apr 9, 2019
Cluster of microcalcifications can be an early sign of breast cancer. In this paper, we propose a novel approach based on convolutional neural networks for the detection and segmentation of microcalcification clusters. In this work, we used 283 mammo...
Radiographic Image Enhancement Calcinosis Mammography Diagnosis, Differential Radiographic Image Interpretation, Computer-Assisted Algorithms Female Reproducibility of Results Humans Breast Neoplasms Neural Networks, Computer
View on PubMed DOI

Contrast enhancement is a prognostic factor in IDH1/2 mutant, but not in wild-type WHO grade II/III glioma as confirmed by machine learning.

European journal of cancer (Oxford, England : 1990) Dec 7, 2018
BACKGROUND: Mutation of the isocitrate dehydrogenase (IDH) gene and co-deletion on chromosome 1p/19q is becoming increasingly relevant for the evaluation of clinical outcome in glioma. Among the imaging parameters, contrast enhancement (CE) in WHO II...
Male Middle Aged Glioma Follow-Up Studies Retrospective Studies Isocitrate Dehydrogenase Humans Contrast Media Female Radiographic Image Enhancement Machine Learning Magnetic Resonance Imaging Prognosis Survival Rate Mutation Brain Neoplasms World Health Organization
View on PubMed DOI

Detection of Breast Cancer with Mammography: Effect of an Artificial Intelligence Support System.

Radiology Nov 20, 2018
Purpose To compare breast cancer detection performance of radiologists reading mammographic examinations unaided versus supported by an artificial intelligence (AI) system. Materials and Methods An enriched retrospective, fully crossed, multireader, ...
Middle Aged Aged ROC Curve Aged, 80 and over Mammography Radiographic Image Enhancement Humans Adult Breast Artificial Intelligence Breast Neoplasms Female
View on PubMed DOI
Popular Topics
Recent Journals