AIMC Journal:
IEEE transactions on medical imaging
Showing 561 to 570 of 696 articles
Transfer Learning for Image Segmentation by Combining Image Weighting and Kernel Learning.
IEEE transactions on medical imaging
Jul 25, 2018
Many medical image segmentation methods are based on the supervised classification of voxels. Such methods generally perform well when provided with a training set that is representative of the test images to the segment. However, problems may arise ...
Supervised Segmentation of Un-Annotated Retinal Fundus Images by Synthesis.
IEEE transactions on medical imaging
Jul 24, 2018
We focus on the practical challenge of segmenting new retinal fundus images that are dissimilar to existing well-annotated data sets. It is addressed in this paper by a supervised learning pipeline, with its core being the construction of a synthetic...
Deep Generative Adversarial Neural Networks for Compressive Sensing MRI.
IEEE transactions on medical imaging
Jul 23, 2018
Undersampled magnetic resonance image (MRI) reconstruction is typically an ill-posed linear inverse task. The time and resource intensive computations require tradeoffs between accuracy and speed. In addition, state-of-the-art compressed sensing (CS)...
Deep Learning Global Glomerulosclerosis in Transplant Kidney Frozen Sections.
IEEE transactions on medical imaging
Jun 27, 2018
Transplantable kidneys are in very limited supply. Accurate viability assessment prior to transplantation could minimize organ discard. Rapid and accurate evaluation of intra-operative donor kidney biopsies is essential for determining which kidneys ...
Deep Recurrent Neural Networks for Prostate Cancer Detection: Analysis of Temporal Enhanced Ultrasound.
IEEE transactions on medical imaging
Jun 25, 2018
Temporal enhanced ultrasound (TeUS), comprising the analysis of variations in backscattered signals from a tissue over a sequence of ultrasound frames, has been previously proposed as a new paradigm for tissue characterization. In this paper, we prop...
H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes.
IEEE transactions on medical imaging
Jun 11, 2018
Liver cancer is one of the leading causes of cancer death. To assist doctors in hepatocellular carcinoma diagnosis and treatment planning, an accurate and automatic liver and tumor segmentation method is highly demanded in the clinical practice. Rece...
Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Training.
IEEE transactions on medical imaging
Jun 1, 2018
To realize the full potential of deep learning for medical imaging, large annotated datasets are required for training. Such datasets are difficult to acquire due to privacy issues, lack of experts available for annotation, underrepresentation of rar...
Structure-Preserving Guided Retinal Image Filtering and Its Application for Optic Disk Analysis.
IEEE transactions on medical imaging
May 21, 2018
Retinal fundus photographs have been used in the diagnosis of many ocular diseases such as glaucoma, pathological myopia, age-related macular degeneration, and diabetic retinopathy. With the development of computer science, computer aided diagnosis h...
Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?
IEEE transactions on medical imaging
May 17, 2018
Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish diagnosis. The automation of the corresponding tasks has thus been th...
Detecting and Locating Gastrointestinal Anomalies Using Deep Learning and Iterative Cluster Unification.
IEEE transactions on medical imaging
May 15, 2018
This paper proposes a novel methodology for automatic detection and localization of gastrointestinal (GI) anomalies in endoscopic video frame sequences. Training is performed with weakly annotated images, using only image-level, semantic labels inste...
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