ArticleProceedings of SPIE--the International Society for Optical Engineering2020
Hyperspectral imaging and deep learning for the detection of breast cancer cells in digitized histological images.
Article in Proceedings of SPIE--the International Society for Optical Engineering, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Medical hyperspectral imaging: an updated review of technology advancements and biomedical applications.Journal of biomedical optics · 2026Pooled it
- High-efficacy and affordable hyperspectral pancreatic tissue image analysis using near-infrared spectroscopy.Journal of pathology informatics · 2026Article
- Performance of Naiive Spectral Geometric Models in Histopathology AI.bioRxiv : the preprint server for biology · 2026Article
- Artificial neural networks as a prognostic tool using hyperspectral imaging on pretherapeutic histopathological specimens of esophageal adenocarcinoma.Journal of cancer research and clinical oncology · 2025Article
- Exploring the role of preprocessing combinations in hyperspectral imaging for deep learning colorectal cancer detection.Scientific reports · 2025Article
- Semantic hyperspectral image synthesis for cross-modality knowledge transfer in surgical data science.International journal of computer assisted radiology and surgery · 2025Article
- Research on a Burn Severity Detection Method Based on Hyperspectral Imaging.Sensors (Basel, Switzerland) · 2025Article
- Systematic Meta-Analysis of Computer-Aided Detection of Breast Cancer Using Hyperspectral Imaging.Bioengineering (Basel, Switzerland) · 2024Review
- Advancements in Hyperspectral Imaging and Computer-Aided Diagnostic Methods for the Enhanced Detection and Diagnosis of Head and Neck Cancer.Biomedicines · 2024Review
- Detection and margin assessment of thyroid carcinoma with microscopic hyperspectral imaging using transformer networks.Journal of biomedical optics · 2024Article
- Hybrid brain tumor classification of histopathology hyperspectral images by linear unmixing and an ensemble of deep neural networks.Healthcare technology letters · 2024Article
- An Analytical Study on the Utility of RGB and Multispectral Imagery with Band Selection for Automated Tumor Grading.Diagnostics (Basel, Switzerland) · 2024Article
- A Video Transformer Network for Thyroid Cancer Detection on Hyperspectral Histologic Images.Proceedings of SPIE--the International Society for Optical Engineering · 2023Article
- Conditional Generative Adversarial Network (cGAN) for Synthesis of Digital Histologic Images from Hyperspectral Images.Proceedings of SPIE--the International Society for Optical Engineering · 2023Article
- Extended Depth of Field Imaging for Mosaic Hyperspectral Images.Proceedings of SPIE--the International Society for Optical Engineering · 2023Article
- Spectral Similarity Measures for In Vivo Human Tissue Discrimination Based on Hyperspectral Imaging.Diagnostics (Basel, Switzerland) · 2023Article
- Deep Learning in Medical Hyperspectral Images: A Review.Sensors (Basel, Switzerland) · 2022Review
- Evaluation of Preprocessing Methods on Independent Medical Hyperspectral Databases to Improve Analysis.Sensors (Basel, Switzerland) · 2022Article
- Unsupervised super-resolution reconstruction of hyperspectral histology images for whole-slide imaging.Journal of biomedical optics · 2022Article
- Automatic detection of head and neck squamous cell carcinoma on histologic slides using hyperspectral microscopic imaging.Journal of biomedical optics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
In recent years, hyperspectral imaging (HSI) has been shown as a promising imaging modality to assist pathologists in the diagnosis of histological samples. In this work, we present the use of HSI for discriminating between normal and tumor breast cancer cells. Our customized HSI system includes a hyperspectral (HS) push-broom camera, which is attached to a standard microscope, and home-made software system for the control of image acquisition. Our HS microscopic system works in the visible and near-infrared (VNIR) spectral range (400 - 1000 nm). Using this system, 112 HS images were captured from histologic samples of human patients using 20× magnification. Cell-level annotations were made by an expert pathologist in digitized slides and were then registered with the HS images. A deep learning neural network was developed for the HS image classification, which consists of nine 2D convolutional layers. Different experiments were designed to split the data into training, validation and testing sets. In all experiments, the training and the testing set correspond to independent patients. The results show an area under the curve (AUCs) of more than 0.89 for all the experiments. The combination of HSI and deep learning techniques can provide a useful tool to aid pathologists in the automatic detection of cancer cells on digitized pathologic images.
Indexed as
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.