ArticleScientific reports2022
Learned end-to-end high-resolution lensless fiber imaging towards real-time cancer diagnosis.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 33 citations in OpenAlex.
- Deep learning in neurosurgery: a systematic literature review with a structured analysis of applications across subspecialties.Frontiers in neurology · 2025Pooled it
- Biomaterials for biomarker imaging and detection.Journal of advanced research · 2026Review
- Optical coherence photoacoustic microscopy for 3D cancer model imaging with AI-assisted organoid analysis.Light, science & applications · 2026Article
- Label-free multiphoton microscopy for intraoperative identification of glioma features and tumor heterogeneity.Frontiers in oncology · 2026Article
- Lensless camera: Unraveling the breakthroughs and prospects.Fundamental research · 2025Review
- Deep learning-based virtual staining, segmentation, and classification in label-free photoacoustic histology of human specimens.Light, science & applications · 2024Article
- Resolution-enhanced multi-core fiber imaging learned on a digital twin for cancer diagnosis.Neurophotonics · 2024Article
- AI-driven projection tomography with multicore fibre-optic cell rotation.Nature communications · 2024Article
- Multimode Optical Interconnects on Silicon Interposer Enable Confidential Hardware-to-Hardware Communication.Sensors (Basel, Switzerland) · 2023Article
- Honeycomb Artifact Removal Using Convolutional Neural Network for Fiber Bundle Imaging.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
Abstract
Recent advances in label-free histology promise a new era for real-time diagnosis in neurosurgery. Deep learning using autofluorescence is promising for tumor classification without histochemical staining process. The high image resolution and minimally invasive diagnostics with negligible tissue damage is of great importance. The state of the art is raster scanning endoscopes, but the distal lens optics limits the size. Lensless fiber bundle endoscopy offers both small diameters of a few 100 microns and the suitability as single-use probes, which is beneficial in sterilization. The problem is the inherent honeycomb artifacts of coherent fiber bundles (CFB). For the first time, we demonstrate an end-to-end lensless fiber imaging with exploiting the near-field. The framework includes resolution enhancement and classification networks that use single-shot CFB images to provide both high-resolution imaging and tumor diagnosis. The well-trained resolution enhancement network not only recovers high-resolution features beyond the physical limitations of CFB, but also helps improving tumor recognition rate. Especially for glioblastoma, the resolution enhancement network helps increasing the classification accuracy from 90.8 to 95.6%. The novel technique enables histological real-time imaging with lensless fiber endoscopy and is promising for a quick and minimally invasive intraoperative treatment and cancer diagnosis in neurosurgery.
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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.