Research graph
References from A protocol for evaluating robustness to H&E staining variation in computational pathology models. Local targets link to admitted publications; unresolved targets remain external evidence.
An international study of stain variability in histopathology using qualitative and quantitative analysis
10.1016/j.jpi.2025.100423 · 2025 · External reference
Fragile neural networks: the importance of image standardization for deep learning in digital pathology
2019 · External reference
The impact of site-specific digital histology signatures on deep learning model accuracy and bias
10.1038/s41467-021-24698-1 · 2021 · External reference
Impact of stain variation and color normalization for prognostic predictions in pathology
2025 · External reference
Measuring domain shift for deep learning in histopathology
10.1109/jbhi.2020.3032060 · 2021 · External reference
Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology
10.1016/j.media.2019.101544 · 2019 · External reference
Deeply supervised two stage generative adversarial network for stain normalization
2025 · External reference
Stain normalization using sparse autoencoders (stanosa): application to digital pathology
10.1016/j.compmedimag.2016.05.003 · 2017 · External reference
A method for normalizing histology slides for quantitative analysis
2009 · External reference
Stainnorm: A lightweight and robust network for stain colour normalization in histopathology
2025 · External reference
H&E stain augmentation improves generalization of convolutional networks for histopathological mitosis detection
2018 · External reference
Adversarial stain transfer for histopathology image analysis
10.1109/tmi.2017.2781228 · 2018 · External reference
Data-driven color augmentation for H&E stained images in computational pathology
10.1016/j.jpi.2022.100183 · 2023 · External reference
Histopathological stain transfer using style transfer network with adversarial loss
2020 · External reference
Randstainna: learning stain-agnostic features from histology slides by bridging stain augmentation and normalization
2022 · External reference
Domain-adversarial neural networks to address the appearance variability of histopathology images
2017 · External reference
Staining invariant features for improving generalization of deep convolutional neural networks in computational pathology
2019 · External reference
A survey of pathology foundation model: Progress and future directions
2025 · External reference
A good feature extractor is all you need for weakly supervised pathology slide classification
2025 · External reference
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Do histopathological foundation models eliminate batch effects? a comparative study
2024 · External reference
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Reet: robustness evaluation and enhancement toolbox for computational pathology
10.1093/bioinformatics/btac315 · 2022 · External reference
Quality control stress test for deep learning-based diagnostic model in digital pathology
10.1038/s41379-021-00859-x · 2021 · External reference
Nuclear segmentation and classification: On color and compression generalization
2022 · External reference
Stress testing pathology models with generated artifacts
10.4103/jpi.jpi_6_21 · 2021 · External reference
From modern CNNs to vision transformers: assessing the performance, robustness, and classification strategies of deep learning models in histopathology
10.1016/j.media.2023.102809 · 2023 · External reference
Impact of tissue staining and scanner variation on the performance of pathology foundation models: a study of sarcomas and their mimics
10.1002/2056-4538.70080 · 2026 · External reference
CNN stability training improves robustness to scanner and IHC-based image variability for epithelium segmentation in cervical histology
2023 · External reference
Assessing the robustness of deep learning-assisted pathological image analysis under practical variables of imaging system
2023 · External reference
Registered multi-device/staining histology image dataset for domain-agnostic machine learning models
10.1038/s41597-024-03122-5 · 2024 · External reference
Landscape of microsatellite instability across 39 cancer types
10.1200/po.17.00073 · 2017 · External reference
Performance evaluation for rapid detection of pan-cancer microsatellite instability with mantis
10.18632/oncotarget.13918 · 2016 · External reference
Comprehensive molecular characterization of human colon and rectal cancer
10.1038/nature11252 · 2012 · External reference
Surgen: 1020 H&E-stained whole-slide images with survival and genetic markers
10.1093/gigascience/giaf086 · 2025 · External reference
Resolution-agnostic tissue segmentation in whole-slide histopathology images with convolutional neural networks
10.7717/peerj.8242 · 2019 · External reference
Towards a general-purpose foundation model for computational pathology
10.1038/s41591-024-02857-3 · 2024 · External reference
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Data-efficient and weakly supervised computational pathology on whole-slide images
10.1038/s41551-020-00682-w · 2021 · External reference
Decoupled weight decay regularization
2019 · External reference
Transformer-based biomarker prediction from colorectal cancer histology: a large-scale multicentric study
10.1016/j.ccell.2023.08.002 · 2023 · External reference
Transformer-based unsupervised contrastive learning for histopathological image classification
10.1016/j.media.2022.102559 · 2022 · External reference
Generalizable biomarker prediction from cancer pathology slides with self-supervised deep learning: a retrospective multi-centric study
2023 · External reference
Retccl: clustering-guided contrastive learning for whole-slide image retrieval
10.1016/j.media.2022.102645 · 2023 · External reference
