ArticleScientific reports2021
Classification of colorectal tissue images from high throughput tissue microarrays by ensemble deep learning methods.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed, 30 citations in OpenAlex.
- Intraoperative margin assessment in colorectal cancer via AI-enhanced multiphoton microscopy.Journal of biomedical optics · 2026Article
- Deep learning for malignancy and tumor origin prediction using cytology or histopathology whole slide images.NPJ digital medicine · 2026Article
- EfficientNetB5-Based Deep Learning for Automated Cancer Detection in Tissue Microarray Images.Journal of Cancer · 2026Article
- Topographic analysis of pancreatic cancer by TMA and digital spatial profiling reveals biological complexity with potential therapeutic implications.Scientific reports · 2024Article
- TMA-Mate: An open-source modular toolkit for constructing tissue microarrays of arbitrary layouts.HardwareX · 2023Article
- 3D multiplexed tissue imaging reconstruction and optimized region of interest (ROI) selection through deep learning model of channels embedding.Frontiers in bioinformatics · 2023Article
- Deep learning-based image analysis predicts PD-L1 status from H&E-stained histopathology images in breast cancer.Nature communications · 2022Article
- COVID-CCD-Net: COVID-19 and colon cancer diagnosis system with optimized CNN hyperparameters using gradient-based optimizer.Medical & biological engineering & computing · 2022Article
- Deep Learning on Histopathological Images for Colorectal Cancer Diagnosis: A Systematic Review.Diagnostics (Basel, Switzerland) · 2022Review
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Tissue microarray (TMA) core images are a treasure trove for artificial intelligence applications. However, a common problem of TMAs is multiple sectioning, which can change the content of the intended tissue core and requires re-labelling. Here, we investigate different ensemble methods for colorectal tissue classification using high-throughput TMAs. Hematoxylin and Eosin (H&E) core images of 0.6 mm or 1.0 mm diameter from three international cohorts were extracted from 54 digital slides (n = 15,150 cores). After TMA core extraction and color enhancement, five different flows of independent and ensemble deep learning were applied. Training and testing data with 2144 and 13,006 cores included three classes: tumor, normal or "other" tissue. Ground-truth data were collected from 30 ngTMA slides (n = 8689 cores). A test augmentation is applied to reduce the uncertain prediction. Predictive accuracy of the best method, namely Soft Voting Ensemble of one VGG and one CapsNet models was 0.982, 0.947 and 0.939 for normal, "other" and tumor, which outperformed to independent or ensemble learning with one base-estimator. Our high-accuracy algorithm for colorectal tissue classification in high-throughput TMAs is amenable to images from different institutions, core sizes and stain intensity. It helps to reduce error in TMA core evaluations with previously given labels.
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