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Convolutional autoencoders are highly connected deep learning networks which can learn salient features within imaging data for the purposes of compression, data recovery, development of classifiers and/or segmentation. In this study an objective analysis of a U-Net convolutional autoencoders for unsupervised image segmentation is conducted with respect to haematoxylin-eosin based ground-truth diagnostic pathology. We find that a light-weight network architecture may provide a suitable segmentation approach for chemical imaging. |
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Image segmentation
Image compression
Imaging spectroscopy
FT-IR spectroscopy
Pathology
Tissues
Data modeling
