Abstract: Deep learning has been successful in classifying the label and severity stage of certain diseases, but few of them explain how to make predictions. We propose to use deep learning in medical diagnosis to…
Abstract: Objective. Intracranial pathologies are treated using mean intracranial pressure (ICP). The ICP pulse waveform, the shape of the ICP signal over one cardiac cycle, also indicates craniospinal space state. This study proposed an end-to-end…
Abstract: This paper trains a deep convolutional neural network with low-bitwidth weights and activations. The quantizer’s non-differentiability makes optimizing a low-precision network difficult, resulting in accuracy loss. Progressive quantization, stochastic precision, and joint knowledge distillation…
Abstract: AI healthcare applications use sensitive EHRs that are rarely labeled and distributed across symbiont institutions. Such data makes machine learning model training difficult. To address these challenges, we propose dynamic neural graphs-based federated learning…
Abstract: This paper uses Deep Learning (DL) on smartphone tri-axial accelerometer signals to identify and verify drivers. ResNet-50 followed by two SGRUs is the driver identification architecture. Shortcut connections allow ResNet to extract rich accelerometer…
Abstract: DPODv2, a dense correspondence-based three-stage 6 DoF object detector, is proposed. A 2D object detector, dense correspondence estimation network, and multi-view pose refinement method estimate a full 6 DoF pose. We propose a unified…
Abstract: Distracted driving deaths have skyrocketed in recent years. Deep learning can detect distracted driving. Real-time detection requires an optimized network with a small number of parameters, high accuracy, and high speed. A decreasing filter…
Abstract: Autonomous vehicle technology requires reliable traffic sign detection and recognition (TSDR). The importance of this task has led to extensive research and many promising methods in the literature. Most of these methods have been…
Abstract: This paper introduces a framework to recover avatar details from a single image. Human shapes, body poses, textures, and viewpoints make it difficult. Previous methods used a parametric template without surface details to recover…
Abstract: Super paramagnetic iron oxide (SPIO) particles are useful for MRI contrast agents due to their susceptibility. Relaxometry or measuring their inhomogeneities quantifies these particles. These methods use phase, which is unreliable at high concentrations….
Abstract: NPC is a malignant epithelial cancer of the nasopharynx. Survival prediction helps NPC patients plan treatments. Deep learning-based survival prediction models have outperformed radiomics-based models recently. Deep survival models use image patches of the…
Abstract: Deep generative models train neural networks to model training sample distributions. Research has fragmented into interconnected approaches that trade run-time, diversity, and architectural restrictions. This compendium covers energy-based models, variational autoencoders, generative adversarial networks,…
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