AIMC Journal:
Medical image analysis

Showing 571 to 580 of 699 articles

Medical image classification using synergic deep learning.

Medical image analysis Feb 18, 2019
The classification of medical images is an essential task in computer-aided diagnosis, medical image retrieval and mining. Although deep learning has shown proven advantages over traditional methods that rely on the handcrafted features, it remains c...
Radiology Information Systems Diagnosis, Computer-Assisted Deep Learning Image Processing, Computer-Assisted Diagnostic Imaging Data Mining Information Storage and Retrieval Humans Neural Networks, Computer
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Evaluating reinforcement learning agents for anatomical landmark detection.

Medical image analysis Feb 14, 2019
Automatic detection of anatomical landmarks is an important step for a wide range of applications in medical image analysis. Manual annotation of landmarks is a tedious task and prone to observer errors. In this paper, we evaluate novel deep reinforc...
Humans Brain Pregnancy Anatomic Landmarks Heart Head Female Deep Learning Adult Imaging, Three-Dimensional Magnetic Resonance Imaging
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Constrained-CNN losses for weakly supervised segmentation.

Medical image analysis Feb 13, 2019
Weakly-supervised learning based on, e.g., partially labelled images or image-tags, is currently attracting significant attention in CNN segmentation as it can mitigate the need for full and laborious pixel/voxel annotations. Enforcing high-order (gl...
Neural Networks, Computer Male Imaging, Three-Dimensional Image Processing, Computer-Assisted Magnetic Resonance Imaging Prostate Humans Magnetic Resonance Imaging, Cine Spine Heart Ventricles Supervised Machine Learning
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OBELISK-Net: Fewer layers to solve 3D multi-organ segmentation with sparse deformable convolutions.

Medical image analysis Feb 13, 2019
Deep networks have set the state-of-the-art in most image analysis tasks by replacing handcrafted features with learned convolution filters within end-to-end trainable architectures. Still, the specifications of a convolutional network are subject to...
Imaging, Three-Dimensional Image Processing, Computer-Assisted Abdomen Viscera Neural Networks, Computer Algorithms Tomography, X-Ray Computed Humans
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Iterative fully convolutional neural networks for automatic vertebra segmentation and identification.

Medical image analysis Feb 12, 2019
Precise segmentation and anatomical identification of the vertebrae provides the basis for automatic analysis of the spine, such as detection of vertebral compression fractures or other abnormalities. Most dedicated spine CT and MR scans as well as s...
Fractures, Compression Magnetic Resonance Imaging Image Interpretation, Computer-Assisted Spinal Fractures Humans Neural Networks, Computer Tomography, X-Ray Computed
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Attention gated networks: Learning to leverage salient regions in medical images.

Medical image analysis Feb 5, 2019
We propose a novel attention gate (AG) model for medical image analysis that automatically learns to focus on target structures of varying shapes and sizes. Models trained with AGs implicitly learn to suppress irrelevant regions in an input image whi...
Female Ultrasonography, Prenatal Imaging, Three-Dimensional Humans Algorithms Neural Networks, Computer Tomography, X-Ray Computed Pregnancy Image Interpretation, Computer-Assisted Datasets as Topic Radiography, Abdominal
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Adversarial training with cycle consistency for unsupervised super-resolution in endomicroscopy.

Medical image analysis Feb 2, 2019
In recent years, endomicroscopy has become increasingly used for diagnostic purposes and interventional guidance. It can provide intraoperative aids for real-time tissue characterization and can help to perform visual investigations aimed for example...
Humans Algorithms Esophagus Video Recording Datasets as Topic Colon Microscopy, Confocal Unsupervised Machine Learning
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f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks.

Medical image analysis Jan 31, 2019
Obtaining expert labels in clinical imaging is difficult since exhaustive annotation is time-consuming. Furthermore, not all possibly relevant markers may be known and sufficiently well described a priori to even guide annotation. While supervised le...
Retina Algorithms Humans Diagnostic Techniques, Ophthalmological Information Theory Image Processing, Computer-Assisted Neural Networks, Computer Tomography, Optical Coherence
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Multi-task exclusive relationship learning for alzheimer's disease progression prediction with longitudinal data.

Medical image analysis Jan 30, 2019
Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive impairment of memory and other cognitive functions. Currently, many multi-task learning approaches have been proposed to predict the disease progression at the earl...
Male Disease Progression Image Processing, Computer-Assisted Humans Female Machine Learning Magnetic Resonance Imaging Aged Computer Simulation Predictive Value of Tests Alzheimer Disease
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Recurrent inference machines for reconstructing heterogeneous MRI data.

Medical image analysis Jan 18, 2019
Deep learning allows for accelerated magnetic resonance image (MRI) reconstruction, thereby shortening measurement times. Rather than using sparsifying transforms, a prerequisite in Compressed Sensing (CS), suitable MRI prior distributions are learne...
Magnetic Resonance Imaging Humans Signal-To-Noise Ratio Neural Networks, Computer Deep Learning Brain Image Processing, Computer-Assisted
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