ArticleCancers2020
Experimental Assessment of Color Deconvolution and Color Normalization for Automated Classification of Histology Images Stained with Hematoxylin and Eosin.
Article in Cancers, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- TOP2 and NOS2 Orchestrate the Generation of DNA Breaks to Promote Colitis Cancer Initiation.Cancers · 2026Article
- Impact of tissue staining and scanner variation on the performance of pathology foundation models: a study of sarcomas and their mimics.The journal of pathology. Clinical research · 2026Article
- Detection, localization, and staging of breast cancer lymph node metastasis in digital pathology whole slide images using selective neighborhood attention-based deep learning.Scientific reports · 2025Article
- Novel feature-based method for multi-modal biomedical image registration compared to intensity-based technique.Scientific reports · 2025Article
- Optimized Whole-Slide-Image H&E Stain Normalization: A Step Towards Big Data Integration in Digital Pathology.IEEE open journal of engineering in medicine and biology · 2025Article
- Beneficial role of rosemary extract on oxidative stress-mediated neuronal apoptosis in rotenone-induced attention deficit hyperactivity disease in juvenile rat model.Acta bio-medica : Atenei Parmensis · 2023Article
- Multiclass classification of breast cancer histopathology images using multilevel features of deep convolutional neural network.Scientific reports · 2022Article
- Validation of a color deconvolution method to quantify MSC tri-lineage differentiation across species.Frontiers in veterinary science · 2022Article
- Digital Pathology Enables Automated and Quantitative Assessment of Inflammatory Activity in Patients with Chronic Liver Disease.Biomolecules · 2021Article
- Spatial immunoprofiling of the intratumoral and peritumoral tissue of renal cell carcinoma patients.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2021Article
- Normalization of HE-stained histological images using cycle consistent generative adversarial networks.Diagnostic pathology · 2021Article
- Comparison of texture-based classification and deep learning for plantar soft tissue histology segmentation.Computers in biology and medicine · 2021Article
- Deep Learning Prediction of Metastasis in Locally Advanced Colon Cancer Using Binary Histologic Tumor Images.Cancers · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Histological evaluation plays a major role in cancer diagnosis and treatment. The appearance of H&E-stained images can vary significantly as a consequence of differences in several factors, such as reagents, staining conditions, preparation procedure and image acquisition system. Such potential sources of noise can all have negative effects on computer-assisted classification. To minimize such artefacts and their potentially negative effects several color pre-processing methods have been proposed in the literature-for instance, color augmentation, color constancy, color deconvolution and color transfer. Still, little work has been done to investigate the efficacy of these methods on a quantitative basis. In this paper, we evaluated the effects of color constancy, deconvolution and transfer on automated classification of H&E-stained images representing different types of cancers-specifically breast, prostate, colorectal cancer and malignant lymphoma. Our results indicate that in most cases color pre-processing does not improve the classification accuracy, especially when coupled with color-based image descriptors. Some pre-processing methods, however, can be beneficial when used with some texture-based methods like Gabor filters and Local Binary Patterns.
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