A recent imaging modality Diffusion Tensor Imaging completes information used from Structural MRI in studies of Alzheimer disease. A large number of recent studies has explored pathologic staging of Alzheimer disease using the Mean Diffusivity maps extracted from the Diffusion Tensor Imaging modality. The Deep Neural Networks are seducing tools for classification of subjects' imaging data in computer-aided diagnosis of Alzheimer's disease. The major problem here is the lack of a publicly available large amount of training data in both modalities. The lack number of training data yields over-fitting phenomena. We propose a method of a cross-modal transfer learning: from Structural MRI to Diffusion Tensor Imaging modality. Models pre-trained on a structural MRI dataset with domain-depended data augmentation are used as initialization of network parameters to train on Mean Diffusivity data. The method shows a reduction of the over-fitting phenomena, improves learning performance, and thus increases the accuracy of prediction. Classifiers are then fused by a majority vote resulting in augmented scores of classification between Normal Control, Alzheimer Patients and Mild Cognitive Impairment subjects on a subset of ADNI dataset.

Alzheimer's Disease (AD) is a neurodegenerative disease characterized by progressive loss of memory and general decline in cognitive functions. Multi-modal imaging such as structural MRI and DTI provide useful information for the classification of patients on the basis of brain biomarkers. Recently, CNN methods have emerged as powerful tools to improve classification using images. In this paper, we propose a transfer learning scheme using Convolutional Neural Networks (CNNs) to automatically classify brain scans focusing only on a small ROI: e.g. a few slices of the hippocampal region. The network's architecture is similar to a LeNet-like CNN upon which models are built and fused for AD stage classification diagnosis. We evaluated various types of transfer learning through the following mechanisms: (i) cross-modal (sMRI and DTI) and (ii) cross-domain transfer learning (using MNIST) (iii) a hybrid transfer learning of both types. Our method shows good performances even on small datasets and with a limited number of slices of small brain region. It increases accuracy with more than 5 points for the most difficult classification tasks, i.e., AD/MCI and MCI/NC. Our methodology provides good accuracy scores for classification over a shallow convolutional network. Besides, we focused only on a small region; i.e., the hippocampal region, where few slices are selected to feed the network. Also, we used cross-modal transfer learning. Our proposed method is suitable for working with a shallow CNN network for low-resolution MRI and DTI scans. It yields to significant results even if the model is trained on small datasets, which is often the case in medical image analysis.

To this day, despite the increasing motor capability of robotic devices, elaborating efficient control strategies is still a key challenge in the field of humanoid robotic arms. In particular, providing a human “pilot” with efficient ways to drive such a robotic arm requires thorough testing prior to integration into a finished system. Additionally, when it is needed to preserve anatomical consistency between pilot and robot, such testing requires to employ devices showing human-like features. To fulfill this need for a biomimetic test platform, we present Reachy, a human-like life-scale robotic arm with seven joints from shoulder to wrist. Although Reachy does not include a poly-articulated hand and is therefore more suitable for studying reaching than manipulation, a robotic hand prototype from available third-party projects could be integrated to it. Its 3D-printed structure and off-the-shelf actuators make it inexpensive relatively to the price of an industrial-grade robot. Using an open-source architecture, its design makes it broadly connectable and customizable, so it can be integrated into many applications. To illustrate how Reachy can connect to external devices, this paper presents several proofs of concept where it is operated with various control strategies, such as tele-operation or gaze-driven control. In this way, Reachy can help researchers to explore, develop and test innovative control strategies and interfaces on a human-like robot.