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About Augmenting Data When Training A CNN For Retinal Vessel Segmentation: How To Warp?
This document discusses augmenting data when training a convolutional neural network (CNN) for retinal vessel segmentation. Deep learning methods like CNNs can learn features from data to perform segmentation, but require large labeled datasets. However, acquiring and annotating medical images is difficult. The document investigates augmenting datasets with warped retinal fundus images to reduce labeling needs while maintaining performance. Nonlinear transformations are used to warp images to generate new samples. Preliminary results suggest the amount of data may be halved while keeping the same segmentation accuracy.
Detailed Information
| Author: | ['mindworkz pro'] |
|---|---|
| Publication Year: | 2019 |
| Pages: | 4 |
| Language: | English |
| Format: | |
| Price: | FREE |
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