ArticleFrontiers in bioengineering and biotechnology2019
Unsupervised Domain Adaptation for Classification of Histopathology Whole-Slide Images.
Article in Frontiers in bioengineering and biotechnology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Virtual histopathology methods in medical imaging - a systematic review.BMC medical imaging · 2024Pooled it
- Automated Assessment of OCT Angiography Image Quality Using the Artificial Intelligence Ready and Exploratory Atlas for Diabetes Insights Data Set.Ophthalmology science · 2026Article
- Article
- Abnormality-aware multimodal learning for WSI classification.Frontiers in medicine · 2025Article
- Computational pathology: A survey review and the way forward.Journal of pathology informatics · 2024Review
- Learning generalizable AI models for multi-center histopathology image classification.NPJ precision oncology · 2024Article
- A New Method of Artificial-Intelligence-Based Automatic Identification of Lymphovascular Invasion in Urothelial Carcinomas.Diagnostics (Basel, Switzerland) · 2024Article
- Artificial Intelligence in Breast Cancer Diagnosis and Personalized Medicine.Journal of breast cancer · 2023Review
- Vessel Delineation Using U-Net: A Sparse Labeled Deep Learning Approach for Semantic Segmentation of Histological Images.Cancers · 2023Article
- Artificial intelligence in breast pathology - dawn of a new era.NPJ breast cancer · 2023Article
- Applications of machine and deep learning to thyroid cytology and histopathology: a review.Frontiers in oncology · 2023Review
- Artificial intelligence in histopathology: enhancing cancer research and clinical oncology.Nature cancer · 2022Review
- Review
- Unleashing the potential of digital pathology data by training computer-aided diagnosis models without human annotations.NPJ digital medicine · 2022Article
- Improving feature extraction from histopathological images through a fine-tuning ImageNet model.Journal of pathology informatics · 2022Article
- Artificial intelligence for precision medicine in autoimmune liver disease.Frontiers in immunology · 2022Review
- Machine learning in renal pathology.Frontiers in nephrology · 2022Article
- Bidirectional Mapping-Based Domain Adaptation for Nucleus Detection in Cross-Modality Microscopy Images.IEEE transactions on medical imaging · 2021Article
- Artificial intelligence and digital pathology: Opportunities and implications for immuno-oncology.Biochimica et biophysica acta. Reviews on cancer · 2021Review
- Generative Adversarial Networks in Digital Pathology and Histopathological Image Processing: A Review.Journal of pathology informatics · 2021Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Computational image analysis is one means for evaluating digitized histopathology specimens that can increase the reproducibility and reliability with which cancer diagnoses are rendered while simultaneously providing insight as to the underlying mechanisms of disease onset and progression. A major challenge that is confronted when analyzing samples that have been prepared at disparate laboratories and institutions is that the algorithms used to assess the digitized specimens often exhibit heterogeneous staining characteristics because of slight differences in incubation times and the protocols used to prepare the samples. Unfortunately, such variations can render a prediction model learned from one batch of specimens ineffective for characterizing an ensemble originating from another site. In this work, we propose to adopt unsupervised domain adaptation to effectively transfer the discriminative knowledge obtained from any given source domain to the target domain without requiring any additional labeling or annotation of images at the target site. In this paper, our team investigates the use of two approaches for performing the adaptation: (1) color normalization and (2) adversarial training. The adversarial training strategy is implemented through the use of convolutional neural networks to find an invariant feature space and Siamese architecture within the target domain to add a regularization that is appropriate for the entire set of whole-slide images. The adversarial adaptation results in significant classification improvement compared with the baseline models under a wide range of experimental settings.
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Registered trials
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.