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The importance of stain normalization in colorectal tissue classification with convolutional networks

arXiv 1702.05931 ↗First posted 20 Feb 2017cs.CV cs.LGcited by 20

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Abstract (arXiv)

The development of reliable imaging biomarkers for the analysis of colorectal cancer (CRC) in hematoxylin and eosin (H&E) stained histopathology images requires an accurate and reproducible classification of the main tissue components in the image. In this paper, we propose a system for CRC tissue classification based on convolutional networks (ConvNets). We investigate the importance of stain normalization in tissue classification of CRC tissue samples in H&E-stained images. Furthermore, we report the performance of ConvNets on a cohort of rectal cancer samples and on an independent publicly available dataset of colorectal H&E images.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Feb 2017.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 4; 2023: 9; 2024: 2; 2025: 1; 2026: 1.

20 papers.

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  1. 2022RandStainNA: Learning Stain-Agnostic Features from Histology Slides by Bridging Stain Augmentation and Normalizationcited by 32
  2. 2021Machine Learning Methods for Histopathological Image Analysis: A Reviewcited by 10
  3. 2026Scanner-Induced Domain Shifts Undermine the Robustness of Pathology Foundation Modelscited by 9
  4. 2022FedDAR: Federated Domain-Aware Representation Learningcited by 8
  5. 2022RestainNet: a self-supervised digital re-stainer for stain normalizationcited by 7
  6. 2024StainFuser: Controlling Diffusion for Faster Neural Style Transfer in Multi-Gigapixel Histology Imagescited by 6
  7. 2022H&E-adversarial network: a convolutional neural network to learn stain-invariant features through Hematoxylin & Eosin regressioncited by 5
  8. 2023Breast Cancer Histopathology Image based Gene Expression Prediction using Spatial Transcriptomics data and Deep Learningcited by 5
  9. 2023Multi-domain stain normalization for digital pathology: A cycle-consistent adversarial network for whole slide imagescited by 4
  10. 2023Physical Color Calibration of Digital Pathology Scanners for Robust Artificial Intelligence Assisted Cancer Diagnosiscited by 4
  11. 2021A Pathology Deep Learning System Capable of Triage of Melanoma Specimens Utilizing Dermatopathologist Consensus as Ground Truthcited by 3
  12. 2023Standardized CycleGAN training for unsupervised stain adaptation in invasive carcinoma classification for breast histopathologycited by 2
8 more
  1. 2023Automatic Data Augmentation Learning using Bilevel Optimization for Histopathological Imagescited by 1
  2. 2021Multi-Channel Auto-Encoders and a Novel Dataset for Learning Domain Invariant Representations of Histopathology Imagescited by 0
  3. 2023ParamNet: A Dynamic Parameter Network for Fast Multi-to-One Stain Normalizationcited by 0
  4. 2023PseudoCell: Hard Negative Mining as Pseudo Labeling for Deep Learning-Based Centroblast Cell Detectioncited by 0
  5. 2023Stain Consistency Learning: Handling Stain Variation for Automatic Digital Pathology Segmentationcited by 0
  6. 2023Semi-supervised ViT knowledge distillation network with style transfer normalization for colorectal liver metastases survival predictioncited by 0
  7. 2024NMGrad: Advancing Histopathological Bladder Cancer Grading with Weakly Supervised Deep Learningcited by 0
  8. 2025Adaptive Stain Normalization for Cross-Domain Medical Histologycited by 0