Published August 8, 2023
| Version v1
Journal article
Open
Biased Deep Learning Methods in Detection of COVID-19 Using CT Images: A Challenge Mounted by Subject-Wise-Split ISFCT Dataset
Creators
- 1. Law, Economics, and Data Science Group, Department of Humanities, Social and Political Science, ETH Zurich, 8092 Zurich, Switzerland
- 2. Institute for Intelligent Systems Research and Innovation, Deakin University, Melbourne, VIC 3125, Australia
- 3. Department of Radiology, School of Medicine, Isfahan University of Medical Sciences, Isfahan JM76+5M3, Iran
- 4. St. George's Hospital, London SW17 0RE, UK
- 5. School of Engineering, University of British Columbia, Kelowna, BC V1V 1V7, Canada
- 6. Cyclica Inc., Toronto, ON M5J 1A7, Canada
- 7. Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan JM76+5M3, Iran
- 8. Department of Engineering, Durham University, Durham DH1 3LE, UK
Description
Our investigation indicate that complex models do not necessarily generate more accurate and repeatable results in strict splitting schema.
Files
Copy_of_DEEPCOVID.ipynb
Files
(2.9 GB)
| Name | Size | Download all |
|---|---|---|
|
md5:3350bb6416bca0f82afc79e0061ceb67
|
3.2 MB | Preview Download |
|
md5:b7ec30b8f984a1cc783672092f9254d5
|
744.1 MB | Preview Download |
|
md5:1081e7ac0607f3f0058db62e5579ff66
|
3.7 MB | Preview Download |
|
md5:c8f8ce6bc44e187be990cc56864d876d
|
1.7 MB | Preview Download |
|
md5:6e9ed80221cab239e9c89158109e3157
|
2.1 GB | Preview Download |