Digital holographic microscopy applied to 3D computer microvision by using deep neural networks [PDF]
EPJ Web of Conferences, 2023 Deep neural networks are increasingly applied in many branches of applied science such as computer vision and image processing by increasing performances of instruments.
Brito Carcaño Jesús E. +6 more
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Federated Reservoir Computing Neural Networks [PDF]
2021 International Joint Conference on Neural Networks (IJCNN), 2021 A critical aspect in Federated Learning is the aggregation strategy for the combination of multiple models, trained on the edge, into a single model that incorporates all the knowledge in the federation. Common Federated Learning approaches for Recurrent Neural Networks (RNNs) do not provide guarantees on the predictive performance of the aggregated ...
Bacciu D. +4 more
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Building a decoder of perceptual decisions from microsaccades and pupil size
Frontiers in Psychology, 2022 Many studies have reported neural correlates of visual awareness across several brain regions, including the sensory, parietal, and frontal areas. In most of these studies, participants were instructed to explicitly report their perceptual experience ...
Ryohei Nakayama +8 more
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Correlation between neural responses and human perception in figure-ground segregation
Frontiers in Systems Neuroscience, 2023 Segmentation of a natural scene into objects (figures) and background (ground) is one of crucial functions for object recognition and scene understanding.
Motofumi Shishikura +3 more
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An overview of neural networks for medical image recognition [PDF]
E3S Web of Conferences, 2023 Medical image recognition plays a crucial role in computer diagnostics and has been greatly enhanced by the advancements in deep learning techniques, particularly neural networks.
Berezovsky V.V., Vygovskaya N.V.
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Pattern Classification of Hand Movement Tremor in MS Patients with DBS ON and OFF [PDF]
Journal of Biomedical Physics and Engineering, 2022 Background: Hand tremor is one of the consequences of MS disease degrading quality of patient’s life. Recently DBS is used as a prominent treatment to reduce this effect.
Fatemeh Valipour, Ali Esteki
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Application of convolutional neural networks for distal radio-ulnar fracture detection on plain radiographs in the emergency room [PDF]
Clinical and Experimental Emergency Medicine, 2021 Objective Recent studies have suggested that deep-learning models can satisfactorily assist in fracture diagnosis. We aimed to evaluate the performance of two of such models in wrist fracture detection.
Min Woong Kim +8 more
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Journal of Imaging, 2023 Artificial neural networks can solve various tasks in computer vision, such as image classification, object detection, and general recognition.
Ladislav Karrach, Elena Pivarčiová
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Enhanced gradient learning for deep neural networks
IET Image Processing, 2022 Deep neural networks have achieved great success in both computer vision and natural language processing tasks. How to improve the gradient flows is crucial in training very deep neural networks. To address this challenge, a gradient enhancement approach
Ming Yan +5 more
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Artificial neural network approach for acute poisoning mortality prediction in emergency departments [PDF]
Clinical and Experimental Emergency Medicine, 2021 Objective The number of deaths due to acute poisoning (AP) is on the increase. It is crucial to predict AP patient mortality to identify those requiring intensive care for providing appropriate patient care as well as preserving medical resources.
Seon Yeong Park +6 more
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