ArticleLight, science & applications2022
GANscan: continuous scanning microscopy using deep learning deblurring.
Article in Light, science & applications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed.
- Deep Learning Integration in Optical Microscopy: Advancements and Applications.Microscopy research and technique · 2026Review
- Nanoscale Imaging of Biological Tissues: Techniques, Challenges and Emerging Frontiers.Nanomaterials (Basel, Switzerland) · 2025Review
- BlurryScope enables compact, cost-effective scanning microscopy for HER2 scoring using deep learning on blurry images.NPJ digital medicine · 2025Article
- Neural network-based processing and reconstruction of compromised biophotonic image data.Light, science & applications · 2024Review
- Stereoscopic video deblurring transformer.Scientific reports · 2024Article
- Enhancing scanning electron microscopy imaging quality of weakly conductive samples through unsupervised learning.Scientific reports · 2024Article
- Non-Invasive Self-Adaptive Information States' Acquisition inside Dynamic Scattering Spaces.Research (Washington, D.C.) · 2024Article
- Deep learning-enabled virtual histological staining of biological samples.Light, science & applications · 2023Review
- Digital staining facilitates biomedical microscopy.Frontiers in bioinformatics · 2023Article
- Deep learning accelerates whole slide imaging for next-generation digital pathology applications.Light, science & applications · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Most whole slide imaging (WSI) systems today rely on the "stop-and-stare" approach, where, at each field of view, the scanning stage is brought to a complete stop before the camera snaps a picture. This procedure ensures that each image is free of motion blur, which comes at the expense of long acquisition times. In order to speed up the acquisition process, especially for large scanning areas, such as pathology slides, we developed an acquisition method in which the data is acquired continuously while the stage is moving at high speeds. Using generative adversarial networks (GANs), we demonstrate this ultra-fast imaging approach, referred to as GANscan, which restores sharp images from motion blurred videos. GANscan allows us to complete image acquisitions at 30x the throughput of stop-and-stare systems. This method is implemented on a Zeiss Axio Observer Z1 microscope, requires no specialized hardware, and accomplishes successful reconstructions at stage speeds of up to 5000 μm/s. We validate the proposed method by imaging H&E stained tissue sections. Our method not only retrieves crisp images from fast, continuous scans, but also adjusts for defocusing that occurs during scanning within +/- 5 μm. Using a consumer GPU, the inference runs at <20 ms/ image.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.