ArticleScientific reports2025
Impact of stain variation and color normalization for prognostic predictions in pathology.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Review
- Comparative analysis of whole-slide scanner tissue detection algorithms: Implications for scan area, scan time, and file size in high-volume digital pathology workflows.Journal of pathology informatics · 2026Article
- A protocol for evaluating robustness to H&E staining variation in computational pathology models.Journal of pathology informatics · 2026Article
- Lessons learned from the validation of a machine learning-based colorectal carcinoma screening pipeline in sub-Saharan Africa.Journal of pathology informatics · 2026Article
- Breast Cancer Hormone Receptor Status Determination from H&E-Stained Biopsy Images Using Pixel-Level Classifiers.Cancers · 2026Article
- Predicting Response to Immune Checkpoint Inhibitors in Melanoma: Emerging Approaches in Digital Pathology, Spatial Profiling and Machine Learning.International journal of molecular sciences · 2026Review
- Mitigating Batch Effects in Histopathology via Language-Mediated Robust Embedding Generation.Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 2026Article
- AI and the digital pathology revolution: clinical applications in cancer diagnosis and assessment.Expert review of molecular diagnostics · 2026Review
- 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
- Staining normalization in histopathology: Method benchmarking using multicenter dataset.Scientific reports · 2026Article
- StainStyleSampler: clustering-based sampling of whole slide image appearances.Diagnostic pathology · 2026Article
- Smart Lies and Sharp Eyes: Pragmatic Artificial Intelligence for Cancer Pathology: Promise, Pitfalls, and Access Pathways.Cancers · 2026Review
- A structure-aware and causal-invariant framework for glioma cell classification in pathological images.Frontiers in cell and developmental biology · 2026Article
- Beyond prediction: AI as a mechanistic microscope and digital twin for colorectal cancer immunotherapy.Frontiers in immunology · 2026Review
- Analytic Fourier ptychotomography for aberration-free and high-resolution volumetric refractive index imaging.Nature communications · 2025Article
- Deep learning enhances precision diagnosis and treatment of non-small cell lung cancer: future prospects.Translational lung cancer research · 2025Review
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Authors and funding
6 authors.
Funding
Abstract
In recent years, deep neural networks (DNNs) have demonstrated remarkable performance in pathology applications, potentially even outperforming expert pathologists due to their ability to learn subtle features from large datasets. One complication in preparing digital pathology datasets for DNN tasks is the variation in tinctorial qualities. A common way to address this is to perform stain normalization on the images. In this study, we show that a well-trained DNN model trained on one batch of histological slides failed to generalize to another batch prepared at a different time from the same tissue blocks, even when stain normalization methods were applied. This study used sample data from a previously reported DNN that was able to identify patients with early-stage non-small cell lung cancer (NSCLC) whose tumors did and did not metastasize, with high accuracy, based on training and then testing of digital images from H&E stained primary tumor tissue sections processed at the same time. In this study, we obtained a new series of histologic slides from the adjacent recuts of the same tissue blocks processed in the same lab but at a different time. We found that the DNN trained on either batch of slides/images was unable to generalize and failed to predict progression in the other batch of slides/images (AUC
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.