Slice-Level-Guided Convolutional Neural Networks to study the Right Ventricular Segmentation using MRI Short-Axis sequences-Reference-Cited by-同舟云学术

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Slice-Level-Guided Convolutional Neural Networks to study the Right Ventricular Segmentation using MRI Short-Axis sequences

Published:2021-11 Issue: Volume: Page:
ISSN:
Container-title:2021 IEEE/ACS 18th International Conference on Computer Systems and Applications (AICCSA)
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Author:

Ammari Asma,Mahmoudi Ramzi,Hmida Badii,Saouli Rachida,Bedoui Mohamed Hedi

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9686796/9686755/09686842.pdf?arnumber=9686842

Reference15 articles.

1. A survey on Image Data Augmentation for Deep Learning

2. OptKeras, a wrapper around Keras and Optuna;minami;Medium,2019

3. Survey of Dropout Methods for Deep Neural Networks;labach;ArXiv190413310 Cs,2019

4. Multi-Scale Context Aggregation by Dilated Convolutions;yu;ArXiv151107122 Cs,2016

5. Dropout: A Simple Way to Prevent Neural Networks from Overfitting’;srivastava;J Mach Learn Res,2014

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Ventricular segmentation and modeling using topological watershed transformation and harmonic state model;International Journal of Imaging Systems and Technology;2022-10-13

2. Clinical-Guided Strategy Towards a Spatio-Temporal Cardiac MRI Right Ventricular Short-Axis (ST-CMRI-RVSA) Labeled Dataset;SN Computer Science;2022-05-13

3. IoT Based Prediction of Active and Passive Earth Pressure Coefficients Using Artificial Neural Networks;Networked Systems;2022

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