ArticleMedical image analysis2020
Graph temporal ensembling based semi-supervised convolutional neural network with noisy labels for histopathology image analysis.
Article in Medical image analysis, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Transcriptomic-guided whole-slide image classification for molecular subtype identification.PLoS computational biology · 2026Article
- BrainGeneBot: a framework for variant prioritization and generative pretrained transformer-informed interpretation across polygenic risk score studies.Briefings in bioinformatics · 2025Article
- Integrated brain connectivity analysis with fMRI, DTI, and sMRI powered by interpretable graph neural networks.Medical image analysis · 2025Article
- Article
- Abnormality-aware multimodal learning for WSI classification.Frontiers in medicine · 2025Article
- Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review.Journal of pathology informatics · 2024Article
- Clinically applicable optimized periprosthetic joint infection diagnosis via AI based pathology.NPJ digital medicine · 2024Article
- Review
- Biological insights and novel biomarker discovery through deep learning approaches in breast cancer histopathology.NPJ breast cancer · 2023Review
- Application of Deep Learning in Histopathology Images of Breast Cancer: A Review.Micromachines · 2022Review
- MixPatch: A New Method for Training Histopathology Image Classifiers.Diagnostics (Basel, Switzerland) · 2022Article
- Brain Functional Connectivity Analysis via Graphical Deep Learning.IEEE transactions on bio-medical engineering · 2022Article
- RCTE: A reliable and consistent temporal-ensembling framework for semi-supervised segmentation of COVID-19 lesions.Information sciences · 2021Article
- A bird's-eye view of deep learning in bioimage analysis.Computational and structural biotechnology journal · 2020Review
- Deep Learning Models for Gastric Signet Ring Cell Carcinoma Classification in Whole Slide Images.Technology in cancer research & treatmentArticle
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Authors and funding
6 authors.
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Abstract
Although convolutional neural networks have achieved tremendous success on histopathology image classification, they usually require large-scale clean annotated data and are sensitive to noisy labels. Unfortunately, labeling large-scale images is laborious, expensive and lowly reliable for pathologists. To address these problems, in this paper, we propose a novel self-ensembling based deep architecture to leverage the semantic information of annotated images and explore the information hidden in unlabeled data, and meanwhile being robust to noisy labels. Specifically, the proposed architecture first creates ensemble targets for feature and label predictions of training samples, by using exponential moving average (EMA) to aggregate feature and label predictions within multiple previous training epochs. Then, the ensemble targets within the same class are mapped into a cluster so that they are further enhanced. Next, a consistency cost is utilized to form consensus predictions under different configurations. Finally, we validate the proposed method with extensive experiments on lung and breast cancer datasets that contain thousands of images. It can achieve 90.5% and 89.5% image classification accuracy using only 20% labeled patients on the two datasets, respectively. This performance is comparable to that of the baseline method with all labeled patients. Experiments also demonstrate its robustness to small percentage of noisy labels.
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