Determining an optimal generalization model with deep neural networks for a medical task is an expensive process that generally requires large amounts of data and computing power. Furthermore, the complexity of the programming expressiveness increases to scale deep learning workflows over new heterogeneous system architectures for training each model and efficiently configure the computing resources. We introduce DiagnoseNET, an automatic framework designed for scaling deep learning models over heterogeneous systems applied to medical diagnosis. DiagnoseNET is designed as a modular framework to enable the deep learning workflow management and allows the expressiveness of neural networks written in TensorFlow, while the DiagnoseNET runtime abstracts the data locality, micro batching and the distributed orchestration to scale the neural network model from a GPU workstation to multi-nodes. The main approach is composed through a set of gradient computation modes to adapt the neural network according to the memory capacity, the workers' number, the coordination method and the communication protocol (GRPC or MPI) for achieving a balance between accuracy and energy consumption. The experiments carried out allow to evaluate the computational performance in terms of accuracy, convergence time and worker scalability to determine an optimal neural architecture over a mini-cluster of Jetson TX2 nodes. These experiments were performed using two medical cases of study, the former dataset is composed by clinical descriptors collected during the first week of hospitalization of patients in the Provence-Alpes-Coe d'Azur region; the second dataset uses a short ECG records between 30 and 60s, obtained as part of the PhysioNet 2017 Challenge.
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