Neural Image Compression
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47b73f2d-c7e3-417c-b2a7-90097ceaa932 — Feb. 5, 2022
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Summary
Compresses Whole Slide Images (WSI) with a convolutional neural network (CNN) described in [1]. A patch of size 128x128x3 is compressed to 128 features. The algorithm saves the compressed slides with additional augmentations (flips and rotations) in the mha format, which can be read with SimpleITK [2].
The WSI must contain a magnification level with 0.5μm pixel spacing (± 0.05).
[1] D. Tellez, D. Hoppener, C. Verhoef, D. Grunhagen, P. Nierop, M. Drozdzal, J. van der Laak, and F. Ciompi, “Extending unsupervised neural image compression with supervised multitask learning,” in Medical Imaging with Deep Learning, 2020. [2] Lowekamp, Bradley Christopher, et al. "The design of SimpleITK." Frontiers in neuroinformatics 7 (2013): 45, https://simpleitk.readthedocs.io
Mechanism
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Interfaces
This algorithm implements all of the following input-output combinations:
| Inputs | Outputs | |
|---|---|---|
| 1 |
Validation and Performance
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Uses and Directions
This algorithm was developed for research purposes only.
Warnings
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Common Error Messages
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Information on this algorithm has been provided by the Algorithm Editors,
following the Model Facts labels guidelines from
Sendak, M.P., Gao, M., Brajer, N. et al.
Presenting machine learning model information to clinical end users with model facts labels.
npj Digit. Med. 3, 41 (2020). 10.1038/s41746-020-0253-3