ArticleJournal of pathology informatics2023
Data-driven color augmentation for H&E stained images in computational pathology.
Article in Journal of pathology informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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24 citing papers in PubMed, 42 citations in OpenAlex.
- A protocol for evaluating robustness to H&E staining variation in computational pathology models.Journal of pathology informatics · 2026Article
- Mitigating Batch Effects in Histopathology via Language-Mediated Robust Embedding Generation.Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 2026Article
- Machine Learning Predicts Adequacy of Rapid On-site Evaluation in Fine Needle Aspirations in Lung Cancer Cytology.The American journal of pathology · 2026Article
- MS-GATOR: multi-scale graph attention with topological reasoning for segmentation and classification of prostate cancer based on Gleason scores using histopathology images.Scientific reports · 2026Article
- StainStyleSampler: clustering-based sampling of whole slide image appearances.Diagnostic pathology · 2026Article
- Self-supervised learning to predict intrahepatic cholangiocarcinoma transcriptomic classes on routine histology.JHEP reports : innovation in hepatology · 2026Article
- Domain-generalizable oral cancer classification: bridging multi-center histopathological datasets.Frontiers in medicine · 2026Article
- Development of a multi-task learning framework with gradnorm for precise wound tissue analysis.PloS one · 2026Article
- Automatic labels are as effective as manual labels in digital pathology images classification with deep learning.Journal of pathology informatics · 2025Article
- Automating liver biopsy segmentation with a robust, open-source tool for pathology research: the HOTSPoT model.NPJ digital medicine · 2025Article
- Bridging Domain Gaps in Computational Pathology: A Comparative Study of Adaptation Strategies.Sensors (Basel, Switzerland) · 2025Article
- Machine learning methods for histopathological image analysis: Updates in 2024.Computational and structural biotechnology journal · 2025Review
- AI-based digital histopathology for perihilar cholangiocarcinoma: A step, not a jump.Journal of pathology informatics · 2024Article
- Towards interactive AI-authoring with prototypical few-shot classifiers in histopathology.Journal of pathology informatics · 2024Article
- A Fully-Automated Senescence Test (FAST) for the high-throughput quantification of senescence-associated markers.GeroScience · 2024Article
- Leveraging immuno-fluorescence data to reduce pathologist annotation requirements in lung tumor segmentation using deep learning.Scientific reports · 2024Article
- Evaluating Cellularity Estimation Methods: Comparing AI Counting with Pathologists' Visual Estimates.Diagnostics (Basel, Switzerland) · 2024Article
- DeepDOF-SE: affordable deep-learning microscopy platform for slide-free histology.Nature communications · 2024Article
- Registered multi-device/staining histology image dataset for domain-agnostic machine learning models.Scientific data · 2024Article
- Artifact Augmentation for Enhanced Tissue Detection in Microscope Scanner Systems.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
11 authors at 4 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computational pathology targets the automatic analysis of Whole Slide Images (WSI). WSIs are high-resolution digitized histopathology images, stained with chemical reagents to highlight specific tissue structures and scanned via whole slide scanners. The application of different parameters during WSI acquisition may lead to stain color heterogeneity, especially considering samples collected from several medical centers. Dealing with stain color heterogeneity often limits the robustness of methods developed to analyze WSIs, in particular Convolutional Neural Networks (CNN), the state-of-the-art algorithm for most computational pathology tasks. Stain color heterogeneity is still an unsolved problem, although several methods have been developed to alleviate it, such as Hue-Saturation-Contrast (HSC) color augmentation and stain augmentation methods. The goal of this paper is to present Data-Driven Color Augmentation (DDCA), a method to improve the efficiency of color augmentation methods by increasing the reliability of the samples used for training computational pathology models. During CNN training, a database including over 2 million H&E color variations collected from private and public datasets is used as a reference to discard augmented data with color distributions that do not correspond to realistic data. DDCA is applied to HSC color augmentation, stain augmentation and H&E-adversarial networks in colon and prostate cancer classification tasks. DDCA is then compared with 11 state-of-the-art baseline methods to handle color heterogeneity, showing that it can substantially improve classification performance on unseen data including heterogeneous color variations.
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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.