ArticleScientific reports2021
A neural pathomics framework for classifying colorectal cancer histopathology images based on wavelet multi-scale texture analysis.
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 21 papers.
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Who cites it
21 citing papers in PubMed.
- Review
- Frequency-domain multi-scale hybrid attention for pathological image classification.Scientific reports · 2026Article
- Detection of collagen band-associated regions in H&E-stained colonic biopsies of collagenous colitis patients using superpixel-based feature extraction and neural network classification.Diagnostic pathology · 2026Article
- AI-based pathomics in kidney diseases: progress and application.Renal failure · 2025Review
- An effective study on the diagnosis of colon cancer with the developed local binary pattern method.Scientific reports · 2025Article
- Radiotranscriptomics of non-small cell lung carcinoma for assessing high-level clinical outcomes using a machine learning-derived multi-modal signature.Biomedical engineering online · 2023Article
- Quantitative analysis of prion disease using an AI-powered digital pathology framework.Scientific reports · 2023Article
- Preparing Data for Artificial Intelligence in Pathology with Clinical-Grade Performance.Diagnostics (Basel, Switzerland) · 2023Review
- The transformational potential of molecular radiomics.Journal of medical radiation sciences · 2023Review
- Possibilities of a Hybrid Method for a Time-Scale-Frequency Analysis in the Aspect of Identifying Surface Topography Irregularities.Materials (Basel, Switzerland) · 2023Article
- Application of artificial intelligence in diagnosis and treatment of colorectal cancer: A novel Prospect.Frontiers in medicine · 2023Review
- Investigating the value of radiomics stemming from DSC quantitative biomarkers in IDH mutation prediction in gliomas.Frontiers in neurology · 2023Article
- EBHI-Seg: A novel enteroscope biopsy histopathological hematoxylin and eosin image dataset for image segmentation tasks.Frontiers in medicine · 2023Article
- AI-Based Pipeline for Classifying Pediatric Medulloblastoma Using Histopathological and Textural Images.Life (Basel, Switzerland) · 2022Article
- A Convolutional Neural Networks-Based Approach for Texture Directionality Detection.Sensors (Basel, Switzerland) · 2022Article
- Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential.Frontiers in oncology · 2022Review
- The relationship between radiomics and pathomics in Glioblastoma patients: Preliminary results from a cross-scale association study.Frontiers in oncology · 2022Article
- Mapping the Corn Residue-Covered Types Using Multi-Scale Feature Fusion and Supervised Learning Method by Chinese GF-2 PMS Image.Frontiers in plant science · 2022Article
- Texture Analysis of Enhanced MRI and Pathological Slides Predicts EGFR Mutation Status in Breast Cancer.BioMed research international · 2022Article
- Deep Radiotranscriptomics of Non-Small Cell Lung Carcinoma for Assessing Molecular and Histology Subtypes with a Data-Driven Analysis.Diagnostics (Basel, Switzerland) · 2021Article
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6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Colorectal cancer (CRC) constitutes the third most commonly diagnosed cancer in males and the second in females. Precise histopathological classification of CRC tissue pathology is the cornerstone not only for diagnosis but also for patients' management decision making. An automated system able to accurately classify different CRC tissue regions may increase diagnostic precision and alleviate clinical workload. However, tissue classification is a challenging task due to the variability in morphological and textural characteristics present in histopathology images. In this study, an artificial neural network was trained to classify between eight classes of CRC tissue image patches derived from a public dataset with 5000 CRC histopathology image tiles. A total of 532 multi-level pathomics features examined at different scales were extracted by visual descriptors such as local binary patterns, wavelet transforms and Gabor filters. An exhaustive evaluation involving a variety of wavelet families and parameters was performed in order to shed light on the impact of scale on pathomics based CRC tissue differentiation. Our model achieved a performance accuracy of 95.3% with tenfold cross validation demonstrating superior performance compared to 87.4% reported in recent studies. Furthermore, we experimentally showed that the first and the second levels of the wavelet approximations can be used without compromising classification performance.
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