ArticleJournal of biomedical optics2019
Super-resolution recurrent convolutional neural networks for learning with multi-resolution whole slide images.
Article in Journal of biomedical optics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Optical super-resolution histology of formalin-fixed paraffin-embedded tissue samples: challenges and opportunities.Nature communications · 2025Review
- Single patch super-resolution of histopathology whole slide images: a comparative study.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- A super-resolution network using channel attention retention for pathology images.PeerJ. Computer science · 2023Article
- Applications of discriminative and deep learning feature extraction methods for whole slide image analysis: A survey.Journal of pathology informatics · 2023Review
- A low-cost pathological image digitalization method based on 5 times magnification scanning.Quantitative imaging in medicine and surgery · 2022Article
- Impact of color augmentation and tissue type in deep learning for hematoxylin and eosin image super resolution.Journal of pathology informatics · 2022Article
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Authors and funding
5 authors.
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Abstract
We study a problem scenario of super-resolution (SR) algorithms in the context of whole slide imaging (WSI), a popular imaging modality in digital pathology. Instead of just one pair of high- and low-resolution images, which is typically the setup in which SR algorithms are designed, we are given multiple intermediate resolutions of the same image as well. The question remains how to best utilize such data to make the transformation learning problem inherent to SR more tractable and address the unique challenges that arises in this biomedical application. We propose a recurrent convolutional neural network model, to generate SR images from such multi-resolution WSI datasets. Specifically, we show that having such intermediate resolutions is highly effective in making the learning problem easily trainable and address large resolution difference in the low and high-resolution images common in WSI, even without the availability of a large size training data. Experimental results show state-of-the-art performance on three WSI histopathology cancer datasets, across a number of metrics.
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Registered trials
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