ArticleFrontiers in bioengineering and biotechnology2019
Assessing the Impact of Color Normalization in Convolutional Neural Network-Based Nuclei Segmentation Frameworks.
Article in Frontiers in bioengineering and biotechnology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed.
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- Article
- Out-of-distribution generalization for segmentation of lymph node metastasis in breast cancer.Scientific reports · 2025Article
- Improving generalization capability of deep learning-based nuclei instance segmentation by non-deterministic train time and deterministic test time stain normalization.Computational and structural biotechnology journal · 2024Article
- OralEpitheliumDB: A Dataset for Oral Epithelial Dysplasia Image Segmentation and Classification.Journal of imaging informatics in medicine · 2024Article
- PseudoCell: Hard Negative Mining as Pseudo Labeling for Deep Learning-Based Centroblast Cell Detection.IEEE open journal of engineering in medicine and biology · 2024Article
- An AI based, open access screening tool for early diagnosis of Burkitt lymphoma.Frontiers in medicine · 2024Article
- Impact of Stain Normalization on Pathologist Assessment of Prostate Cancer: A Comparative Study.Cancers · 2023Article
- StainCUT: Stain Normalization with Contrastive Learning.Journal of imaging · 2022Article
- Explainable Biomarkers for Automated Glomerular and Patient-Level Disease Classification.Kidney360 · 2022Article
- Machine learning in renal pathology.Frontiers in nephrology · 2022Article
- Stain normalization in digital pathology: Clinical multi-center evaluation of image quality.Journal of pathology informatics · 2022Article
- MMO-Net (Multi-Magnification Organ Network): A use case for Organ Identification using Multiple Magnifications in Preclinical Pathology Studies.Journal of pathology informatics · 2022Article
- Impact of scanner variability on lymph node segmentation in computational pathology.Journal of pathology informatics · 2022Article
- Diagnosis of Leukaemia in Blood Slides Based on a Fine-Tuned and Highly Generalisable Deep Learning Model.Sensors (Basel, Switzerland) · 2021Article
- A large-scale internal validation study of unsupervised virtual trichrome staining technologies on nonalcoholic steatohepatitis liver biopsies.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2021Article
- Multimarginal Wasserstein Barycenter for Stain Normalization and Augmentation.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2020Article
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6 authors.
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Abstract
Image analysis tools for cancer, such as automatic nuclei segmentation, are impacted by the inherent variation contained in pathology image data. Convolutional neural networks (CNN), demonstrate success in generalizing to variable data, illustrating great potential as a solution to the problem of data variability. In some CNN-based segmentation works for digital pathology, authors apply color normalization (CN) to reduce color variability of data as a preprocessing step prior to prediction, while others do not. Both approaches achieve reasonable performance and yet, the reasoning for utilizing this step has not been justified. It is therefore important to evaluate the necessity and impact of CN for deep learning frameworks, and its effect on downstream processes. In this paper, we evaluate the effect of popular CN methods on CNN-based nuclei segmentation frameworks.
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