AIMC Topic: Image Enhancement
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Multi-channel multi-scale fully convolutional network for 3D perivascular spaces segmentation in 7T MR images.
Medical image analysis
Feb 27, 2018
Accurate segmentation of perivascular spaces (PVSs) is an important step for quantitative study of PVS morphology. However, since PVSs are the thin tubular structures with relatively low contrast and also the number of PVSs is often large, it is chal...
Deep learning approaches for detection and removal of ghosting artifacts in MR spectroscopy.
Magnetic resonance in medicine
Feb 1, 2018
Respiratory motion correction for free-breathing 3D abdominal MRI using CNN-based image registration: a feasibility study.
The British journal of radiology
Jan 31, 2018
OBJECTIVE: Free-breathing abdomen imaging requires non-rigid motion registration of unavoidable respiratory motion in three-dimensional undersampled data sets. In this work, we introduce an image registration method based on the convolutional neural ...
EAC-Net: Deep Nets with Enhancing and Cropping for Facial Action Unit Detection.
IEEE transactions on pattern analysis and machine intelligence
Jan 10, 2018
In this paper, we propose a deep learning based approach for facial action unit (AU) detection by enhancing and cropping regions of interest of face images. The approach is implemented by adding two novel nets (a.k.a. layers): the enhancing layers an...
Fuzziness-based active learning framework to enhance hyperspectral image classification performance for discriminative and generative classifiers.
PloS one
Jan 5, 2018
Hyperspectral image classification with a limited number of training samples without loss of accuracy is desirable, as collecting such data is often expensive and time-consuming. However, classifiers trained with limited samples usually end up with a...
Computer-aided diagnosis of contrast-enhanced spectral mammography: A feasibility study.
European journal of radiology
Dec 5, 2017
OBJECTIVE: To evaluate whether the use of a computer-aided diagnosis-contrast-enhanced spectral mammography (CAD-CESM) tool can further increase the diagnostic performance of CESM compared with that of experienced radiologists.
Machine Learning in Radiology: Applications Beyond Image Interpretation.
Journal of the American College of Radiology : JACR
Nov 17, 2017
Much attention has been given to machine learning and its perceived impact in radiology, particularly in light of recent success with image classification in international competitions. However, machine learning is likely to impact radiology outside ...
Towards machine learned quality control: A benchmark for sharpness quantification in digital pathology.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Sep 25, 2017
Pathology is on the verge of a profound change from an analog and qualitative to a digital and quantitative discipline. This change is mostly driven by the high-throughput scanning of microscope slides in modern pathology departments, reaching tens o...
Artificial Intelligence: Threat or Boon to Radiologists?
Journal of the American College of Radiology : JACR
Aug 19, 2017
The development and integration of machine learning/artificial intelligence into routine clinical practice will significantly alter the current practice of radiology. Changes in reimbursement and practice patterns will also continue to affect radiolo...
Ensemble of expert deep neural networks for spatio-temporal denoising of contrast-enhanced MRI sequences.
Medical image analysis
Aug 2, 2017
Dynamic contrast-enhanced MRI (DCE-MRI) is an imaging protocol where MRI scans are acquired repetitively throughout the injection of a contrast agent. The analysis of dynamic scans is widely used for the detection and quantification of blood-brain ba...
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