mlsanityData to 7 Oct 2026

Physical Color Calibration of Digital Pathology Scanners for Robust Artificial Intelligence Assisted Cancer Diagnosis

arXiv 2307.05519 ↗First posted 7 Jul 2023q-bio.QM cs.AI cs.CVcited by 4

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Abstract (arXiv)

The potential of artificial intelligence (AI) in digital pathology is limited by technical inconsistencies in the production of whole slide images (WSIs), leading to degraded AI performance and posing a challenge for widespread clinical application as fine-tuning algorithms for each new site is impractical. Changes in the imaging workflow can also lead to compromised diagnoses and patient safety risks. We evaluated whether physical color calibration of scanners can standardize WSI appearance and enable robust AI performance. We employed a color calibration slide in four different laboratories and evaluated its impact on the performance of an AI system for prostate cancer diagnosis on 1,161 WSIs. Color standardization resulted in consistently improved AI model calibration and significant improvements in Gleason grading performance. The study demonstrates that physical color calibration provides a potential solution to the variation introduced by different scanners, making AI-based cancer diagnostics more reliable and applicable in clinical settings.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 16 of its 64 references that are in the map. First posted on arXiv 7 Jul 2023.

Builds on

16 of 64 references.

References · 16

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2022Generative Adversarial Networkscited by 5,603
  3. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
  4. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
  5. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
  6. 2016TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systemscited by 660
  7. 2017Unsupervised Image-to-Image Translation Networkscited by 602
  8. 2017DualGAN: Unsupervised Dual Learning for Image-to-Image Translationcited by 310
  9. 2017Learning to Discover Cross-Domain Relations with Generative Adversarial Networkscited by 278
  10. 2019U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translationcited by 186
  11. 2019Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathologycited by 172
  12. 2018Distribution Matching Losses Can Hallucinate Features in Medical Image Translationcited by 99
4 more
  1. 2019Automated Gleason Grading of Prostate Biopsies using Deep Learningcited by 69
  2. 2019Deep learning-based survival prediction for multiple cancer types using histopathology imagescited by 47
  3. 2020StainNet: a fast and robust stain normalization networkcited by 28
  4. 2017The importance of stain normalization in colorectal tissue classification with convolutional networkscited by 20

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Citing papers per year: 2025: 4.

4 papers.

  1. 2025The PAR dataset: Prostate biopsy whole slide images from an underrepresented Middle Eastern populationcited by 2
  2. 2025Validation of Diagnostic Artificial Intelligence Models for Prostate Pathology in a Middle Eastern Cohortcited by 1
  3. 2025Artificial Intelligence-Assisted Prostate Cancer Diagnosis for Reduced Use of Immunohistochemistrycited by 0
  4. 2025Staining normalization in histopathology: Method benchmarking using multicenter datasetcited by 0