Long-term cognitive decline prediction based on multi-modal data using Multimodal3DSiameseNet: transfer learning from Alzheimer's disease to Parkinson's disease.

Purpose: Monitoring and predicting the cognitive state of subjects with neurodegenerative disorders is crucial to provide appropriate treatment as soon as possible. In this work, we present a machine learning approach using multimodal data (brain MRI and clinical) from two early medical visits, to predict the longer-term cognitive decline of patients. Using transfer learning, our model can be successfully transferred from one neurodegenerative disease (Alzheimer's) to another (Parkinson's).

Attention-guided neural network for early dementia detection using MRS data.

Imaging bio-markers have been widely used for Computer-Aided Diagnosis (CAD) of Alzheimer's Disease (AD) with Deep Learning (DL). However, the structural brain atrophy is not detectable at an early stage of the disease (namely for Mild Cognitive Impairment (MCI) and Mild Alzheimer's Disease (MAD)). Indeed, potential biological bio-markers have been proved their ability to early detect brain abnormalities related to AD before brain structural damage and clinical manifestation.

A Hybrid Method for 3D Reconstruction of MR Images.

Three-dimensional surface reconstruction is a well-known task in medical imaging. In procedures for intervention or radiation treatment planning, the generated models should be accurate and reflect the natural appearance. Traditional methods for this task, such as Marching Cubes, use smoothing post processing to reduce staircase artifacts from mesh generation and exhibit the natural look.

A multi-sequences MRI deep framework study applied to glioma classfication.

Glioma is one of the most important central nervous system tumors, ranked 15th in the most common cancer for men and women. Magnetic Resonance Imaging (MRI) represents a common tool for medical experts to the diagnosis of glioma. A set of multi-sequences from an MRI is selected according to the severity of the pathology.

Investigating efficient CNN architecture for multiple sclerosis lesion segmentation.

The automatic segmentation of multiple sclerosis lesions in magnetic resonance imaging has the potential to reduce radiologists' efforts on a daily time-consuming task and to bring more reproducibility. Almost all new segmentation techniques make use of convolutional neural networks with their own different architecture. Architectural choices are rarely explained.

Ex vivo PBMC cytokine profile in familial Mediterranean fever patients: Involvement of IL-1β, IL-1α and Th17-associated cytokines and decrease of Th1 and Th2 cytokines.

In order to clarify the inflammatory mechanism underlying familial Mediterranean fever (FMF), we aimed to evaluate the ex vivo cytokine profile of FMF patients during acute attacks and attack-free periods, and compare it with that of healthy controls. The study included 34 FMF patients, of whom 9 were studied during attack and remission and 24 healthy controls. Cytokine levels were evaluated by Luminex technology in serum and supernatants of PBMC (Peripheral Blood Mononuclear Cells) cultures with and without 24h stimulation of monocytes by LPS and T lymphocytes by anti-CD3/CD28 beads.