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About Active Deep Learning For Medical Imaging Segmentation
This document proposes an active learning framework using convolutional neural networks for medical image segmentation with limited labeled training data. The approach uses dropout at test time to model pixel-wise uncertainty and select the most informative unlabeled samples for manual labeling. Results on a skin lesion dataset showed 74% segmentation accuracy after 9 iterations, demonstrating the potential of this cost-effective active learning method for medical imaging tasks.
Detailed Information
| Author: | ['ram avasthi'] |
|---|---|
| Publication Year: | 2019 |
| Pages: | 3 |
| Language: | English |
| Format: | |
| Price: | FREE |
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