ArticleAPL photonics2020
Label-free colorectal cancer screening using deep learning and spatial light interference microscopy (SLIM).
Article in APL photonics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 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
20 citing papers in PubMed, 52 citations in OpenAlex.
- Spatial microfluidic holographic integrated platform for label-free and high-dimensional analysis of cancer heterogeneity.Nature communications · 2025Article
- Revealing 3D microanatomical structures of unlabeled thick cancer tissues using holotomography and virtual H&E staining.Nature communications · 2025Article
- Efficient colorectal polyp segmentation using wavelet transformation and AdaptUNet: A hybrid U-Net.Heliyon · 2024Article
- Perspective on quantitative phase imaging to improve precision cancer medicine.Journal of biomedical optics · 2024Article
- Article
- On the use of deep learning for phase recovery.Light, science & applications · 2024Review
- Artificial intelligence-enabled quantitative phase imaging methods for life sciences.Nature methods · 2023Review
- Tryp: a dataset of microscopy images of unstained thick blood smears for trypanosome detection.Scientific data · 2023Article
- Quantification of structural heterogeneity in H&E stained clear cell renal cell carcinoma using refractive index tomography.Biomedical optics express · 2023Article
- Stain-free identification of cell nuclei using tomographic phase microscopy in flow cytometry.Nature photonics · 2022Article
- White blood cell detection, classification and analysis using phase imaging with computational specificity (PICS).Scientific reports · 2022Article
- Automatic Colorectal Cancer Screening Using Deep Learning in Spatial Light Interference Microscopy Data.Cells · 2022Article
- Deep Learning in Biomedical Optics.Lasers in surgery and medicine · 2021Review
- Label-free screening of brain tissue myelin content using phase imaging with computational specificity (PICS).APL photonics · 2021Article
- Spatial light interference microscopy: principle and applications to biomedicine.Advances in optics and photonics · 2021Article
- Computational interference microscopy enabled by deep learning.APL photonics · 2021Article
- Single-cell cytometry via multiplexed fluorescence prediction by label-free reflectance microscopy.Science advances · 2021Article
- Article
- Label-Free White Blood Cell Classification Using Refractive Index Tomography and Deep Learning.BME frontiers · 2021Article
- Deep learning-based light scattering microfluidic cytometry for label-free acute lymphocytic leukemia classification.Biomedical optics express · 2020Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Current pathology workflow involves staining of thin tissue slices, which otherwise would be transparent, followed by manual investigation under the microscope by a trained pathologist. While the hematoxylin and eosin (H&E) stain is well-established and a cost-effective method for visualizing histology slides, its color variability across preparations and subjectivity across clinicians remain unaddressed challenges. To mitigate these challenges, recently we have demonstrated that spatial light interference microscopy (SLIM) can provide a path to intrinsic, objective markers, that are independent of preparation and human bias. Additionally, the sensitivity of SLIM to collagen fibers yields information relevant to patient outcome, which is not available in H&E. Here, we show that deep learning and SLIM can form a powerful combination for screening applications: training on 1,660 SLIM images of colon glands and validating on 144 glands, we obtained a benign vs. cancer classification accuracy of 99%. We envision that the SLIM whole slide scanner presented here paired with artificial intelligence algorithms may prove valuable as a pre-screening method, economizing the clinician's time and effort.
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.