Neural Image Compression

A library that compresses Whole Slide Images (WSI) with a convolutional neural network (CNN) as 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).

The library was described in Tellez D, Litjens G, van der Laak J, Ciompi F. Neural Image Compression for Gigapixel Histopathology Image Analysis. IEEE Trans Pattern Anal Mach Intell. 2021;43(2):567-578.

[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,