ArticleScientific reports2023
Deep learning for multi-class semantic segmentation enables colorectal cancer detection and classification in digital pathology images.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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22 citing papers in PubMed, 52 citations in OpenAlex.
- Artificial Intelligence-Based Histopathology Segmentation for Resource-Constrained Healthcare Systems.Diagnostics (Basel, Switzerland) · 2026Article
- MS-GATOR: multi-scale graph attention with topological reasoning for segmentation and classification of prostate cancer based on Gleason scores using histopathology images.Scientific reports · 2026Article
- Elevated PGE2 and COX-2 expression in villous adenoma: Implications for electrolyte depletion syndrome.Experimental and therapeutic medicine · 2026Article
- Exploring Deep Learning Models for Small Histopathology Datasets: Segmentation and Classification of Glomerular Crescent Lesions with Ablation, Interpretability, and Calibration Analyses.Interdisciplinary sciences, computational life sciences · 2026Article
- Weakly supervised colorectal gland segmentation through self-supervised learning and attention-based pseudo-labeling.Scientific reports · 2026Article
- Research on liver cancer pathology image recognition based on deep learning image processing.Scientific reports · 2026Article
- Diagnostic accuracy of deep learning models for colorectal cancer detection in histopathological whole-slide images: a systematic review and diagnostic test accuracy meta-analysis.Frontiers in oncology · 2026Review
- Weakly supervised regression enables interpretable tumor detection in whole-slide histopathology without negative cases.Scientific reports · 2025Article
- Hybrid deep learning with attention fusion for enhanced colon cancer detection.Scientific reports · 2025Article
- STARC-9: A Large-scale Dataset for Multi-Class Tissue Classification for CRC Histopathology.ArXiv · 2025Article
- Impact of the COVID-19 Pandemic on Colorectal Cancer Surgery: Surgical Outcomes and Tumor Characteristics in a Multicenter Retrospective Cohort.Journal of clinical medicine · 2025Article
- Label-free optical microscopy with artificial intelligence: a new paradigm in pathology.Biophotonics discovery · 2025Review
- Article
- STARC-9: A Large-scale Dataset for Multi-Class Tissue Classification for CRC Histopathology.Advances in neural information processing systems · 2025Article
- Deep learning-based classification of colorectal cancer in histopathology images for category detection.Biology methods & protocols · 2025Article
- MOTH: Memory-Efficient On-the-Fly Tiling of Histological Image Annotations Using QuPath.Journal of imaging · 2024Article
- [Research progress on colorectal cancer identification based on convolutional neural network].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Review
- OralEpitheliumDB: A Dataset for Oral Epithelial Dysplasia Image Segmentation and Classification.Journal of imaging informatics in medicine · 2024Article
- An interpretable machine learning system for colorectal cancer diagnosis from pathology slides.NPJ precision oncology · 2024Article
- Artificial intelligence algorithms for real-time detection of colorectal polyps during colonoscopy: a review.American journal of cancer research · 2024Review
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Authors and funding
8 authors at 4 institutions in 3 countries.
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No grant is acknowledged in the PubMed record.
Abstract
In colorectal cancer (CRC), artificial intelligence (AI) can alleviate the laborious task of characterization and reporting on resected biopsies, including polyps, the numbers of which are increasing as a result of CRC population screening programs ongoing in many countries all around the globe. Here, we present an approach to address two major challenges in the automated assessment of CRC histopathology whole-slide images. We present an AI-based method to segment multiple ([Formula: see text]) tissue compartments in the H &E-stained whole-slide image, which provides a different, more perceptible picture of tissue morphology and composition. We test and compare a panel of state-of-the-art loss functions available for segmentation models, and provide indications about their use in histopathology image segmentation, based on the analysis of (a) a multi-centric cohort of CRC cases from five medical centers in the Netherlands and Germany, and (b) two publicly available datasets on segmentation in CRC. We used the best performing AI model as the basis for a computer-aided diagnosis system that classifies colon biopsies into four main categories that are relevant pathologically. We report the performance of this system on an independent cohort of more than 1000 patients. The results show that with a good segmentation network as a base, a tool can be developed which can support pathologists in the risk stratification of colorectal cancer patients, among other possible uses. We have made the segmentation model available for research use on https://grand-challenge.org/algorithms/colon-tissue-segmentation/ .
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