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Training deep learning models for unbiased stereology requires a large data set with associated ground truth. However manual ground truth annotation is tedious, time-consuming, and expert dependent. We propose an active deep learning method for automatic stereology counts using a snapshot ensemble approach. The method provides a confidence score for each mask in an unlabeled pool that reduces user verification to only images with high information content for training the deep learning model. The proposed method reduces the error rate to less than 1% for unbiased stereology cell counts on immunostained brain cells compared to manual stereology and requires ~25% less expert verification time compared to a previously proposed iterative deep learning approach.
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Iterative Deep Learning Based Unbiased Stereology with Human-in-the-Loop
Lack of enough labeled data is a major problem in building machine learning based models when the manual annotation (labeling) is error-prone, expensive, tedious, and time-consuming. In this paper, we introduce an iterative deep learning based method to improve segmentation and counting of cells based on unbiased stereology applied to regions of interest of extended depth of field (EDF) images. This method uses an existing machine learning algorithm called the adaptive segmentation algorithm (ASA) to generate masks (verified by a user) for EDF images to train deep learning models. Then an iterative deep learning approach is used to feed newly predicted and accepted deep learning masks/images (verified by a user) to the training set of the deep learning model. The error rate in unbiased stereology count of cells on an unseen test set reduced from about 3 % to less than 1 % after 5 iterations of the iterative deep learning based unbiased stereology process.
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- Award ID(s):
- 1746511
- PAR ID:
- 10085308
- Date Published:
- Journal Name:
- 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)
- Page Range / eLocation ID:
- 665 to 670
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICMLA.2018.00106
