ArticleNeurocomputing2016
A Deep Convolutional Neural Network for segmenting and classifying epithelial and stromal regions in histopathological images.
Article in Neurocomputing, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 133 papers.
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133 citing papers in PubMed.
- Deep-learning-based artificial intelligence approaches for grading and progression prediction of clear cell renal cell carcinoma.iScience · 2026Article
- Deep learning-driven recognition of panoramic tumor microenvironment features in H&E sections and its application.Journal for immunotherapy of cancer · 2026Review
- HistoSweep enables cellular-resolution tissue quality control for gigapixel images in digital pathology and spatial omics.bioRxiv : the preprint server for biology · 2026Article
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- AI-Powered Histology for Molecular Profiling in Brain Tumors: Toward Smart Diagnostics from Tissue.Cancers · 2025Review
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- MacNet: a mobile attention classification network combining convolutional neural network and transformer for the differentiation of cervical cancer.Quantitative imaging in medicine and surgery · 2025Article
- MVT-Net: A novel cervical tumour segmentation using multi-view feature transfer learning.PloS one · 2025Article
- Mathematical modelling and deep learning algorithms to automate assessment of single and digitally multiplexed immunohistochemical stains in tumoural stroma.Journal of pathology informatics · 2024Article
- Computational pathology: A survey review and the way forward.Journal of pathology informatics · 2024Review
- Lung and Colon Cancer Detection Using a Deep AI Model.Cancers · 2024Article
- Efficient feature selection for histopathological image classification with improved multi-objective WOA.Scientific reports · 2024Article
- Metabolic light absorption, scattering, and emission (MetaLASE) microscopy.Science advances · 2024Article
- A novel embedded kernel CNN-PCFF algorithm for breast cancer pathological image classification.Scientific reports · 2024Article
- Discovery and generalization of tissue structures from spatial omics data.Cell reports methods · 2024Article
- An improved 3D-UNet-based brain hippocampus segmentation model based on MR images.BMC medical imaging · 2024Article
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- Model-Agnostic Binary Patch Grouping for Bone Marrow Whole Slide Image Representation.The American journal of pathology · 2024Article
- Application of Machine Learning in Chronic Kidney Disease: Current Status and Future Prospects.Biomedicines · 2024Review
- Deep learning system for true- and pseudo-invasion in colorectal polyps.Scientific reports · 2024Article
73 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors.
Funding
Abstract
Epithelial (EP) and stromal (ST) are two types of tissues in histological images. Automated segmentation or classification of EP and ST tissues is important when developing computerized system for analyzing the tumor microenvironment. In this paper, a Deep Convolutional Neural Networks (DCNN) based feature learning is presented to automatically segment or classify EP and ST regions from digitized tumor tissue microarrays (TMAs). Current approaches are based on handcraft feature representation, such as color, texture, and Local Binary Patterns (LBP) in classifying two regions. Compared to handcrafted feature based approaches, which involve task dependent representation, DCNN is an end-to-end feature extractor that may be directly learned from the raw pixel intensity value of EP and ST tissues in a data driven fashion. These high-level features contribute to the construction of a supervised classifier for discriminating the two types of tissues. In this work we compare DCNN based models with three handcraft feature extraction based approaches on two different datasets which consist of 157 Hematoxylin and Eosin (H&E) stained images of breast cancer and 1376 immunohistological (IHC) stained images of colorectal cancer, respectively. The DCNN based feature learning approach was shown to have a F1 classification score of 85%, 89%, and 100%, accuracy (ACC) of 84%, 88%, and 100%, and Matthews Correlation Coefficient (MCC) of 86%, 77%, and 100% on two H&E stained (NKI and VGH) and IHC stained data, respectively. Our DNN based approach was shown to outperform three handcraft feature extraction based approaches in terms of the classification of EP and ST regions.
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