AIMC Journal:
Medical image analysis

Showing 461 to 470 of 684 articles

Fully automatic brain tumor segmentation with deep learning-based selective attention using overlapping patches and multi-class weighted cross-entropy.

Medical image analysis Apr 29, 2020
In this paper, we present a new Deep Convolutional Neural Networks (CNNs) dedicated to fully automatic segmentation of Glioblastoma brain tumors with high- and low-grade. The proposed CNNs model is inspired by the Occipito-Temporal pathway which has ...
Attention Entropy Deep Learning Image Processing, Computer-Assisted Brain Neoplasms Humans
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Designing weighted correlation kernels in convolutional neural networks for functional connectivity based brain disease diagnosis.

Medical image analysis Apr 23, 2020
Functional connectivity networks (FCNs) based on functional magnetic resonance imaging (fMRI) have been widely applied to analyzing and diagnosing brain diseases, such as Alzheimer's disease (AD) and its prodrome stage, i.e., mild cognitive impairmen...
Magnetic Resonance Imaging Cognitive Dysfunction Brain Mapping Neural Networks, Computer Brain Humans
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Surgical spectral imaging.

Medical image analysis Apr 13, 2020
Recent technological developments have resulted in the availability of miniaturised spectral imaging sensors capable of operating in the multi- (MSI) and hyperspectral imaging (HSI) regimes. Simultaneous advances in image-processing techniques and ar...
Machine Learning Image Processing, Computer-Assisted Artificial Intelligence Diagnostic Imaging Humans
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Cellular community detection for tissue phenotyping in colorectal cancer histology images.

Medical image analysis Apr 13, 2020
Classification of various types of tissue in cancer histology images based on the cellular compositions is an important step towards the development of computational pathology tools for systematic digital profiling of the spatial tumor microenvironme...
Neural Networks, Computer Algorithms Humans Cell Nucleus Histological Techniques Tumor Microenvironment Colorectal Neoplasms
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Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation.

Medical image analysis Apr 3, 2020
The medical imaging literature has witnessed remarkable progress in high-performing segmentation models based on convolutional neural networks. Despite the new performance highs, the recent advanced segmentation models still require large, representa...
Neural Networks, Computer Humans Diagnostic Imaging Deep Learning
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An integrated deep learning framework for joint segmentation of blood pool and myocardium.

Medical image analysis Mar 5, 2020
Simultaneous and automatic segmentation of the blood pool and myocardium is an important precondition for early diagnosis and pre-operative planning in patients with complex congenital heart disease. However, due to the high diversity of cardiovascul...
Imaging, Three-Dimensional Myocardium Magnetic Resonance Imaging Humans Deep Learning
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Deep learning uncertainty and confidence calibration for the five-class polyp classification from colonoscopy.

Medical image analysis Feb 28, 2020
There are two challenges associated with the interpretability of deep learning models in medical image analysis applications that need to be addressed: confidence calibration and classification uncertainty. Confidence calibration associates the class...
Deep Learning Reproducibility of Results Uncertainty Calibration Bayes Theorem Humans Colonoscopy
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Automatic labeling of cortical sulci using patch- or CNN-based segmentation techniques combined with bottom-up geometric constraints.

Medical image analysis Feb 28, 2020
The extreme variability of the folding pattern of the human cortex makes the recognition of cortical sulci, both automatic and manual, particularly challenging. Reliable identification of the human cortical sulci in its entirety, is extremely difficu...
Humans Neural Networks, Computer Image Processing, Computer-Assisted Machine Learning
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Synthesized 7T MRI from 3T MRI via deep learning in spatial and wavelet domains.

Medical image analysis Feb 19, 2020
Ultra-high field 7T MRI scanners, while producing images with exceptional anatomical details, are cost prohibitive and hence highly inaccessible. In this paper, we introduce a novel deep learning network that fuses complementary information from spat...
Humans Deep Learning Magnetic Resonance Imaging
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Multi-task learning for the segmentation of organs at risk with label dependence.

Medical image analysis Feb 7, 2020
Automatic segmentation of organs at risk is crucial to aid diagnoses and remains a challenging task in medical image analysis domain. To perform the segmentation, we use multi-task learning (MTL) to accurately determine the contour of organs at risk ...
Neural Networks, Computer Tomography, X-Ray Computed Humans Thorax Organs at Risk Radiotherapy
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