"Eight pruning deep learning models for low storage and high-speed COVID-19 ..."

Mohit Agarwal et al. (2022)

> Home

Details and statistics

DOI: 10.1016/J.COMPBIOMED.2022.105571

access: closed

type: Journal Article

metadata version: 2023-09-30

- view
  - electronic edition via DOI
  - unpaywalled version
  authority control:
- export record
  dblp key:
  - journals/cbm/AgarwalASCGCPDM22
- ask others
- share record
  persistent URL:
  - https://dblp.org/rec/journals/cbm/AgarwalASCGCPDM22
Mohit Agarwal, Sushant Agarwal, Luca Saba, Gian Luca Chabert, Suneet K. Gupta, Alessandro Carriero, Alessio Pasche, Pietro Danna, Armin Mehmedovic, Gavino Faa, Saurabh Shrivastava, Kanishka Jain, Harsh Jain, Tanay Jujaray, Inder M. Singh, Monika Turk, Paramjit S. Chadha, Amer M. Johri, Narendra N. Khanna, Sophie Mavrogeni, John R. Laird, David W. Sobel, Martin Miner, Antonella Balestrieri, Petros P. Sfikakis, George Tsoulfas, Durga Prasanna Misra, Vikas Agarwal, George D. Kitas, Jagjit S. Teji, Mustafa Al-Maini, Surinder K. Dhanjil, Andrew Nicolaides, Aditya Sharma, Vijay Rathore, Mostafa Fatemi, Azra Alizad, Pudukode R. Krishnan, Rajanikant R. Yadav, Frence Nagy, Zsigmond Tamás Kincses, Zoltan Ruzsa, Subbaram Naidu, Klaudija Viskovic, Manudeep K. Kalra, Jasjit S. Suri:
Eight pruning deep learning models for low storage and high-speed COVID-19 computed tomography lung segmentation and heatmap-based lesion localization: A multicenter study using COVLIAS 2.0. Comput. Biol. Medicine 146: 105571 (2022)

manage site settings

To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.