ArticleMedical image analysis2021
Selective synthetic augmentation with HistoGAN for improved histopathology image classification.
Article in Medical image analysis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Automated detection of cerebral microbleeds on ex-vivo MRI scans of community-based older adults.NeuroImage · 2026Article
- Pathology-informed Generative Adversarial Network Augmentation Improves Classification of Peripheral Nerve Sheath Tumors by Modeling Morphological Variability: A Pilot Investigation.Head and neck pathology · 2026Article
- Enhancing Melanoma Diagnosis in Histopathology with Deep Learning and Synthetic Data Augmentation.Bioengineering (Basel, Switzerland) · 2025Article
- Article
- Expert-guided StyleGAN2 image generation elevates AI diagnostic accuracy for maxillary sinus lesions.Communications medicine · 2025Article
- Review
- Deep learning-based image analysis in muscle histopathology using photo-realistic synthetic data.Communications medicine · 2025Article
- Hybrid generative adversarial network based on frequency and spatial domain for histopathological image synthesis.BMC bioinformatics · 2025Article
- Computational methods for metastasis detection in lymph nodes and characterization of the metastasis-free lymph node microarchitecture: A systematic-narrative hybrid review.Journal of pathology informatics · 2024Review
- Collagen fiber centerline tracking in fibrotic tissue via deep neural networks with variational autoencoder-based synthetic training data generation.Medical image analysis · 2023Article
- Artifact Augmentation for Enhanced Tissue Detection in Microscope Scanner Systems.Sensors (Basel, Switzerland) · 2023Article
- Preparing Data for Artificial Intelligence in Pathology with Clinical-Grade Performance.Diagnostics (Basel, Switzerland) · 2023Review
- A Large-scale Synthetic Pathological Dataset for Deep Learning-enabled Segmentation of Breast Cancer.Scientific data · 2023Article
- Deep Active Learning for Automatic Mitotic Cell Detection on HEp-2 Specimen Medical Images.Diagnostics (Basel, Switzerland) · 2023Article
- SIAN: STYLE-GUIDED INSTANCE-ADAPTIVE NORMALIZATION FOR MULTI-ORGAN HISTOPATHOLOGY IMAGE SYNTHESIS.Proceedings. IEEE International Symposium on Biomedical Imaging · 2023Article
- A CAD system for automatic dysplasia grading on H&E cervical whole-slide images.Scientific reports · 2023Article
- Explainable synthetic image generation to improve risk assessment of rare pediatric heart transplant rejection.Journal of biomedical informatics · 2023Article
- Medical Specialty Classification Based on Semiadversarial Data Augmentation.Computational intelligence and neuroscience · 2023Article
- An improved multi-scale gradient generative adversarial network for enhancing classification of colorectal cancer histological images.Frontiers in oncology · 2023Article
- Conditional TransGAN-Based Data Augmentation for PCB Electronic Component Inspection.Computational intelligence and neuroscience · 2023Article
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Authors and funding
10 authors.
Funding
Abstract
Histopathological analysis is the present gold standard for precancerous lesion diagnosis. The goal of automated histopathological classification from digital images requires supervised training, which requires a large number of expert annotations that can be expensive and time-consuming to collect. Meanwhile, accurate classification of image patches cropped from whole-slide images is essential for standard sliding window based histopathology slide classification methods. To mitigate these issues, we propose a carefully designed conditional GAN model, namely HistoGAN, for synthesizing realistic histopathology image patches conditioned on class labels. We also investigate a novel synthetic augmentation framework that selectively adds new synthetic image patches generated by our proposed HistoGAN, rather than expanding directly the training set with synthetic images. By selecting synthetic images based on the confidence of their assigned labels and their feature similarity to real labeled images, our framework provides quality assurance to synthetic augmentation. Our models are evaluated on two datasets: a cervical histopathology image dataset with limited annotations, and another dataset of lymph node histopathology images with metastatic cancer. Here, we show that leveraging HistoGAN generated images with selective augmentation results in significant and consistent improvements of classification performance (6.7% and 2.8% higher accuracy, respectively) for cervical histopathology and metastatic cancer datasets.
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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.