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References from Benefits of high-magnification and color information in computational pathology—An exploratory review. Local targets link to admitted publications; unresolved targets remain external evidence.
The role of whole slide imaging in AI-based digital pathology: current challenges and future directions—an updated literature review
10.3390/jmp7010002 · 2026 · External reference
A survey on deep learning in medical image analysis
10.1016/j.media.2017.07.005 · 2017 · External reference
Built to last? Reproducibility and reusability of deep learning algorithms in computational pathology
10.1016/j.modpat.2023.100350 · 2024 · External reference
Artificial intelligence and computational pathology
10.1038/s41374-020-00514-0 · 2021 · External reference
Deep learning in radiology: an overview of the concepts and a survey of the state of the art with focus on MRI
10.1002/jmri.26534 · 2019 · External reference
The future of radiology: the path towards multimodal AI and superdiagnostics
2025 · External reference
Review of deep learning methods for remote sensing satellite images classification: experimental survey and comparative analysis
10.1186/s40537-023-00772-x · 2023 · External reference
A generalized deep learning framework for whole-slide image segmentation and analysis
10.1038/s41598-021-90444-8 · 2021 · External reference
Reproducibility of deep learning in digital pathology whole slide image analysis
10.1371/journal.pdig.0000145 · 2022 · External reference
Unresolved reference
2025 · External reference
Unresolved reference
2025 · External reference
Expertise in clinical pathology: combining the visual and cognitive perspective
10.1007/s10459-015-9589-x · 2015 · External reference
Impact of image resizing on deep learning detectors for training time and model performance
2022 · External reference
Learning to resize images for computer vision tasks
2021 · External reference
Bag of tricks for image classification with convolutional neural networks
2019 · External reference
Unresolved reference
2017 · External reference
Efficient high-resolution deep learning: a survey
10.1145/3645107 · 2024 · 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
Stain normalization methods for histopathology image analysis: a comprehensive review and experimental comparison
10.1016/j.inffus.2023.101997 · 2024 · External reference
Minimum resolution requirements of digital pathology images for accurate classification
10.1016/j.media.2023.102891 · 2023 · External reference
The effect of spatial resolution on deep learning classification of lung cancer histopathology
2023 · External reference
To what extent does downsampling, compression, and data scarcity impact renal image analysis?
2019 · External reference
The impacts of training data spatial resolution on deep learning in remote sensing
10.1016/j.srs.2024.100185 · 2025 · External reference
What kind of spatial and spectral resolution of UAV-borne hyperspectral image is required for precise crop classification when using deep learning
2022 · External reference
The effect of image resolution on deep learning in radiography, radiology
2020 · External reference
Assessing the impact of image resolution on deep learning for TB lesion segmentation on frontal chest X-rays
10.3390/diagnostics13040747 · 2023 · External reference
Effect of the pixel interpolation method for downsampling medical images on deep learning accuracy
10.4236/jcc.2021.911010 · 2021 · External reference
Unresolved reference
2023 · External reference
BACH: grand challenge on breast cancer histology images
10.1016/j.media.2019.05.010 · 2019 · External reference
A dataset for breast cancer histopathological image classification
10.1109/tbme.2015.2496264 · 2016 · External reference
Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer
10.1001/jama.2017.14585 · 2017 · External reference
Comprehensive genomic characterization of squamous cell lung cancers
10.1038/nature11404 · 2012 · External reference
Comprehensive molecular profiling of lung adenocarcinoma
10.1038/nature13385 · 2014 · External reference
Attention-based color spatial relationship matrix for optimizing deep learning performance in histopathology image grayscale conversion
2025 · External reference
Semantic segmentation and colorization of grayscale aerial imagery with W-Net models
10.1111/exsy.12622 · 2020 · External reference
Robustness of machine learning to color, size change, normalization, and image enhancement on micrograph datasets with large sample differences
10.1016/j.matdes.2023.112086 · 2023 · External reference
Evaluating the impact of color information in deep neural networks
2019 · External reference
Impact of colour on robustness of deep neural networks
2021 · External reference
Exploring effects of colour and image quality in semantic segmentation by deep learning methods
10.2352/j.imagingsci.technol.2022.66.5.050401 · 2022 · External reference
Using grayscale images for object recognition with convolutional-recursive neural network
2016 · External reference
Our deep CNN face matchers have developed achromatopsia
2024 · External reference
What’s color got to do with it? Face recognition in grayscale
10.1109/tbiom.2025.3542316 · 2025 · External reference
Model performance evaluation for color and grayscale images in malaria classification using deep CNN
2024 · External reference
Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge
10.1038/s41591-021-01620-2 · 2022 · External reference
Comparison of CNN- and transformer-based architectures for automated oral epithelium segmentation on whole slide images
2023 · External reference
GrandQC: a comprehensive solution to quality control problem in digital pathology
10.1038/s41467-024-54769-y · 2024 · External reference
