ArticleScientific reports2016
Multi-class texture analysis in colorectal cancer histology.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 101 papers.
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
101 citing papers in PubMed.
- Trial
- CSNet: Cognitive Simulation Network for Reliable Histopathology-Based Cancer Diagnosis.Journal of imaging informatics in medicine · 2026Article
- An Adaptive Attention-Driven Quadruplet Deep Hashing Method for Retrieving Histopathological Images.Journal of imaging · 2026Article
- Artificial Intelligence-Based Histopathology Segmentation for Resource-Constrained Healthcare Systems.Diagnostics (Basel, Switzerland) · 2026Article
- [An auxiliary diagnosis system for cervical intraepithelial neoplasia based on colposcopic images].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Article
- [Colorectal cancer diagnosis method based on dynamic gland-aware and tissue soft-clustering].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Article
- A generalist biomedical vision-language model via multi-CLIP knowledge distillation.Nature communications · 2026Article
- Compositional and interpretable representation of histology using AI foundation models and sparse autoencoders.bioRxiv : the preprint server for biology · 2026Article
- Continual Learning for Histopathology Image Classification in Class-Incremental Learning.Diagnostics (Basel, Switzerland) · 2026Article
- MCLAM: a cost-effective deep learning model for predicting recurrence risk in HR+/HER2- breast cancer-a multi-center study in a Chinese cohort.Journal of the National Cancer Center · 2026Article
- Ensemble of Handcrafted and Learned Features for Colorectal Cancer Classification.Journal of imaging informatics in medicine · 2026Article
- Large-Scale Histological Image Dataset with Metadata for Colorectal Cancer Microenvironment.Scientific data · 2026Article
- Transfer learning with Bayesian optimization for colorectal cancer histopathology classification.BMC medical imaging · 2026Article
- Colorectal Lesion diagnosis using transformer and deep learning with multiscale feature interface.PloS one · 2026Article
- Efficient and explainable histopathology for cancer detection using dual-teacher distillation and integrated gradients.Frontiers in medicine · 2026Article
- Article
- Random features meet MIL: a deep GP approach to colorectal MSI prediction.NPJ digital medicine · 2025Article
- Human visual attention-inspired knowledge distillation underlying interpretable computational pathology.Scientific reports · 2025Article
- Automated tumor stroma ratio assessment in colorectal cancer using hybrid deep learning approach.Scientific reports · 2025Article
- PathOrchestra: a comprehensive foundation model for computational pathology with over 100 diverse clinical-grade tasks.NPJ digital medicine · 2025Article
41 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Automatic recognition of different tissue types in histological images is an essential part in the digital pathology toolbox. Texture analysis is commonly used to address this problem; mainly in the context of estimating the tumour/stroma ratio on histological samples. However, although histological images typically contain more than two tissue types, only few studies have addressed the multi-class problem. For colorectal cancer, one of the most prevalent tumour types, there are in fact no published results on multiclass texture separation. In this paper we present a new dataset of 5,000 histological images of human colorectal cancer including eight different types of tissue. We used this set to assess the classification performance of a wide range of texture descriptors and classifiers. As a result, we found an optimal classification strategy that markedly outperformed traditional methods, improving the state of the art for tumour-stroma separation from 96.9% to 98.6% accuracy and setting a new standard for multiclass tissue separation (87.4% accuracy for eight classes). We make our dataset of histological images publicly available under a Creative Commons license and encourage other researchers to use it as a benchmark for their studies.
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.