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- Title
Deep representation learning for domain adaptable classification of infrared spectral imaging data.
- Authors
Raulf, Arne P; Butke, Joshua; Küpper, Claus; Großerueschkamp, Frederik; Gerwert, Klaus; Mosig, Axel
- Abstract
Motivation Applying infrared microscopy in the context of tissue diagnostics heavily relies on computationally preprocessing the infrared pixel spectra that constitute an infrared microscopic image. Existing approaches involve physical models, which are non-linear in nature and lead to classifiers that do not generalize well, e.g. across different types of tissue preparation. Furthermore, existing preprocessing approaches involve iterative procedures that are computationally demanding, so that computation time required for preprocessing does not keep pace with recent progress in infrared microscopes which can capture whole-slide images within minutes. Results We investigate the application of stacked contractive autoencoders as an unsupervised approach to preprocess infrared microscopic pixel spectra, followed by supervised fine-tuning to obtain neural networks that can reliably resolve tissue structure. To validate the robustness of the resulting classifier, we demonstrate that a network trained on embedded tissue can be transferred to classify fresh frozen tissue. The features obtained from unsupervised pretraining thus generalize across the large spectral differences between embedded and fresh frozen tissue, where under previous approaches separate classifiers had to be trained from scratch. Availability and implementation Our implementation can be downloaded from https://github.com/arnrau/SCAE%5fIR%5fSpectral%5fImaging. Supplementary information Supplementary data are available at Bioinformatics online.
- Subjects
INFRARED imaging; DEEP learning; INFRARED microscopy; INFRARED spectra; SPECTRAL imaging; CLASSIFICATION
- Publication
Bioinformatics, 2020, Vol 36, Issue 1, p287
- ISSN
1367-4803
- Publication type
Article
- DOI
10.1093/bioinformatics/btz505