Deep efficient-nets with transfer learning assisted detection of COVID-19 using chest X-ray radiology imaging.

Corona Virus (COVID-19) could be considered as one of the most devastating pandemics of the twenty-first century. The effective and the rapid screening of infected patients could reduce the mortality and even the contagion rate. Chest X-ray radiology could be designed as one of the effective screening techniques for COVID-19 exploration.

Deep Convolutional Encoder-Decoder algorithm for MRI brain reconstruction.

Compressed Sensing Magnetic Resonance Imaging (CS-MRI) could be considered a challenged task since it could be designed as an efficient technique for fast MRI acquisition which could be highly beneficial for several clinical routines. In fact, it could grant better scan quality by reducing motion artifacts amount as well as the contrast washout effect. It offers also the possibility to reduce the exploration cost and the patient's anxiety.

Relative Ectopic Kidney Function Quantification Using DMSA Tomoscintigraphy Modality.

The ectopic renal function estimation based on a manual region of interest (ROI) extraction could be considered as time consuming. It could also affect the clinical interpretation and thus deviate the therapeutic attitude. For this purpose, we propose an advanced tool to evaluate such function through the dimercaptosuccinic acid (DMSA) kidney scintigraphy scans.

Deep Multi-Scale 3D Convolutional Neural Network (CNN) for MRI Gliomas Brain Tumor Classification.

Accurate and fully automatic brain tumor grading from volumetric 3D magnetic resonance imaging (MRI) is an essential procedure in the field of medical imaging analysis for full assistance of neuroradiology during clinical diagnosis. We propose, in this paper, an efficient and fully automatic deep multi-scale three-dimensional convolutional neural network (3D CNN) architecture for glioma brain tumor classification into low-grade gliomas (LGG) and high-grade gliomas (HGG) using the whole volumetric T1-Gado MRI sequence. Based on a 3D convolutional layer and a deep network, via small kernels, the proposed architecture has the potential to merge both the local and global contextual information with reduced weights.

Denoising and contrast-enhancement approach of magnetic resonance imaging glioblastoma brain tumors.

We investigate a new preprocessing approach for MRI glioblastoma brain tumors. Based on combined denoising technique (bilateral filter) and contrast-enhancement technique (automatic contrast stretching based on image statistical information), the proposed approach offers competitive results while preserving the tumor region's edges and original image's brightness. In order to evaluate the proposed approach's performance, quantitative evaluation has been realized through the Multimodal Brain Tumor Segmentation (BraTS 2015) dataset.

Rank-Two NMF Clustering for Glioblastoma Characterization.

This study investigates a novel classification method for 3D multimodal MRI glioblastomas tumor characterization. We formulate our segmentation problem as a linear mixture model (LMM). Thus, we provide a nonnegative matrix from every MRI slice in every segmentation process' step.

Towards a Computer Aided Prognosis for Brain Glioblastomas Tumor Growth Estimation.

Bridging the gap between mathematical and biological models and clinical applications could be considered as one of the new challenges of medical image analysis over the ten last years. This paper presents an advanced and convivial algorithm for brain glioblastomas tumor growth modelization. The brain glioblastomas tumor region would be extracted using a fast distribution matching developed algorithm based on global pixel wise information.

3D multimodal MRI brain glioma tumor and edema segmentation: a graph cut distribution matching approach.

This study investigates a fast distribution-matching, data-driven algorithm for 3D multimodal MRI brain glioma tumor and edema segmentation in different modalities. We learn non-parametric model distributions which characterize the normal regions in the current data. Then, we state our segmentation problems as the optimization of several cost functions of the same form, each containing two terms: (i) a distribution matching prior, which evaluates a global similarity between distributions, and (ii) a smoothness prior to avoid the occurrence of small, isolated regions in the solution.