A Self-Adaptive Deep Learning Method for Automated Eye Laterality Detection Based on Color Fundus Photography
- 1. School of Computer Science, University of Technology, Sydney, Australia
- 2. State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
- 3. Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Australia
- 4. Guangzhou Healgoo Interactive Medical Technology Co. Ltd., Guangzhou, China
Description
The data for "A Self-Adaptive Deep Learning Method for Automated Eye Laterality Detection Based on Color Fundus Photography", including original image data and labels, DL model files, training logs and validation results.
Content:
- Ex1_Preprocessing # the first experiment: preprocessing methods comparison
- history # validation results of different metrics of models
- log # training record of models. Read by TensorBoard
- model # model file. Read by Keras
- Ex2_Self-adaptive model # the second experiment: development of eye laterality detection model
- data # serialized image data, including data and ground-truth label. Read by Python Pickle package.
- Xy.h5 # training data
- test_Xy.h5 # testing data
- history # validation results of different metrics of models
- log # training record of models. Read by TensorBoard
- model # model file. Read by Keras
- with_adaptive # with self-adaptive strategy
- without_adaptive # without self-adaptive strategy
For code review, please see: https://github.com/keepgallop/Eye-laterality-detection
For more information, please contact liuchi_email@foxmail.com
Files
Published_Data.zip
Files
(5.4 GB)
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md5:6647513378d68018768ee991200fb467
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Additional details
Related works
- Is documented by
- 10.1371/journal.pone.0222025 (DOI)