Haut PDF Lentigo detection using a deep learning approach
Lentigo detection using a deep learning approach
... RCM, Lentigo detection, CNN classifica- tion, ...is a modality in- creasingly used in medical ...in a short ...takes a long time for dermatologists to make full use of the possibilities ...
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Lentigo detection using a deep learning approach
... RCM, Lentigo detection, CNN classifica- tion, ...is a modality in- creasingly used in medical ...in a short ...takes a long time for dermatologists to make full use of the possibilities ...
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Efficient deep learning model for mitosis detection using breast histopathology images
... Mitosis detection is one of the critical factors of cancer prognosis, carrying significant diagnostic information required for breast cancer ...is a very labor- intensive and challenging ...propose a ...
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2020 — A contribution to online tool wear detection using deep learning methodology
... during deep twist drilling process and compared vibration and force signals for this purpose using time and frequency domain fault ...breakage detection in milling ...is a powerful ...in ...
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Learning of Binocular Fixations using Anomaly Detection with Deep Reinforcement Learning
... estimation, learning has to deal with very noisy rewards, which is not the case in most deep reinforcement learning ...used a moving average filter on the temporal reward signal with good ...
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A Novel Deep Learning Approach for Liver MRI Classification and HCC Detection
... proposes a deep learning algorithm based on the Convolu- tional Neural Network (CNN) architecture to detect HepatoCellular Carcinoma (HCC) from liver DCE-MRI (Dynamic Contrast-Enhanced MRI) ...The ...
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Preterm Newborn Presence Detection in Incubator and Open Bed Using Deep Transfer Learning
... be a promising approach in neonatal intensive care units for monitoring the state of preterm newborns since it is contact-less and ...pose a method for automatic detection of preterm newborn ...
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A deep learning approach to classify atherosclerosis using intracoronary optical coherence tomography
... is a fiber-based intravascular imaging modality that produces high-resolution tomographic images of artery lumen and vessel wall ...present a deep learning method that subdivides the whole ...
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Unsupervised Change Detection Analysis in Satellite Image Time Series using Deep Learning Combined with Graph-Based Approaches
... the detection and the analysis of such phenomena as deforestation and droughts [2], [3], [4], [5], real-time monitoring of natural disasters [6], [7], study of the evolution of urbanization [8], crop changes ...
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Robustness of multimodal 3D object detection using deep learning approach for autonomous vehicles
... before a practical self-driving car can operate without human intervention under all diverse conditions [ 2 ...have a long way to go before becoming widely ...
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Energy management for electric vehicles in smart cities: a deep learning approach
... is a mechanism allowing to select the best route based on different information such as congestion, accident, ...presented a novel system called the intention-aware routing system (IARS) for ...compute ...
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Looking for Mimicry in a Snake Assemblage Using Deep Learning
... DCNN-based approach is its ability to suggest mimicry even when resemblance is ...(for a detailed list, see Kikuchi and Pfennig 2013; Dalziell and Welbergen 2016) could apply to Western Palearctic snakes, ...
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Deep Learning Structural and Historical Features for Anti-Patterns Detection
... For a method, the “entity set” contains the entities accessed by the method, and for an attribute, it contains the methods accessing the ...identified using a hierarchical agglomerative algorithm on ...
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Classifying logistic vehicles in cities using Deep learning
... as a result the use of delivery trucks and light commercial vehicles is ...as a tool to monitor the presence of delivery vehicles in order to implement intelligent city planning ...proposes a ...
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A pattern reordering approach based on ambiguity detection for on-line category learning
... competitive learning (UCL) neural networks were used as clusterers with sequential, batch, and reordered ...trial, a fi ed-length data set was normalized using a linear transformation such ...
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Lightweight material acquisition using deep learning
... our deep network imposes some limitations on the type of images and materials we can ...represents a promising direction to scale our approach to high-resolution inputs [ CK17 , KALL18 ...As a ...
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Deep learning for cloud detection
... of a ConvNet architecture applied to patches with the one obtained with a simple neural net with classical handcrafted ...and a CNN architecture applied to ...learnt using the same training ...
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Force-Torque Sensor Disturbance Observer using Deep Learning
... on a recurrent neural network that estimates the non-contact forces measured by a force-torque sensor attached at the end-effector of a robotic ...The approach is proven to also work with an ...
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A Deep Learning Approach for Objective-Driven All-Dielectric Metasurface Design
... to a presumed fabrication constraint. A minimum amplitude transmission threshold of ...m. A total of 93 samples, with a radius step of 5 nm, are chosen to sketch the phase coverage range in ...
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Deep Learning Approach for Postprocessing Regularization in Seizure Preduction
... seizures, a sudden high amplitude discharge evolve to all the areas of the ...by a high temporal resolution and acceptable spatial resolution (especially ...be a result of environmental factors such ...
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