Accelerating a Morphology-Preserving Adsorption Model by Deep Learning-Reference-Cited by-同舟云学术

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Accelerating a Morphology-Preserving Adsorption Model by Deep Learning

Published:2022-10-16 Issue: Volume: Page:
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Container-title:2022 IEEE International Conference on Image Processing (ICIP)
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Author:

Hammoumi Adam¹,Moreaud Maxime¹,Jolimaitre Elsa¹,Chevalier Thibaud²,Klotz Michaela³,Novikov Alexey³

Affiliation:

1. IFP Energies Nouvelles,Solaize,France,69360

2. IFP Energies Nouvelles,Rueil-Malmaison,France,92852

3. LSFC Laboratoire de Synthèse et Fonctionnalisation des Céramiques UMR 3080 CNRS/SAINT-GOBAIN CREE,Saint-Gobain Research Provence,Cavaillon,France,84300

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9897158/9897159/09897406.pdf?arnumber=9897406

Reference19 articles.

1. Efficacy evaluation of 2D, 3D U-Net semantic segmentation and atlas-based segmentation of normal lungs excluding the trachea and main bronchi

2. Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

3. Adding geodesic information and stochastic patch-wise image prediction for small dataset learning

4. Comparison of 2d vs 3d u-net organ segmentation in abdominal 3d ct images;zettler,2021

5. Unet++: A nested u-net architecture for medical image segmentation;zhou;Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support,2018

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