Article... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics2017
Predicting Heart Rejection Using Histopathological Whole-Slide Imaging and Deep Neural Network with Dropout.
Article in ... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Systematic review of generative adversarial networks (GANs) in cell microscopy: Trends, practices, and impact on image augmentation.PloS one · 2025Pooled it
- Machine learning and artificial intelligence in cardiac transplantation: A systematic review.Artificial organs · 2022Pooled it
- Artificial intelligence and machine learning in heart and lung transplantation.EClinicalMedicine · 2026Review
- An In-depth overview of artificial intelligence (AI) tool utilization across diverse phases of organ transplantation.Journal of translational medicine · 2025Review
- The transformative potential of artificial intelligence in solid organ transplantation.Frontiers in transplantation · 2024Review
- Prediction of heart transplant rejection from routine pathology slides with self-supervised deep learning.European heart journal. Digital health · 2023Article
- Explainable synthetic image generation to improve risk assessment of rare pediatric heart transplant rejection.Journal of biomedical informatics · 2023Article
- Deep learning-enabled assessment of cardiac allograft rejection from endomyocardial biopsies.Nature medicine · 2022Article
- Improving Classification of Breast Cancer by Utilizing the Image Pyramids of Whole-Slide Imaging and Multi-Scale Convolutional Neural Networks.Proceedings : Annual International Computer Software and Applications Conference. COMPSAC · 2019Article
- Improved Prediction on Heart Transplant Rejection Using Convolutional Autoencoder and Multiple Instance Learning on Whole-Slide Imaging.... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics · 2019Article
- Prediction of Heart Transplant Rejection Using Histopathological Whole-Slide Imaging.... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Cardiac allograft rejection is one major limitation for long-term survival for patients with heart transplants. The endomyocardial biopsy is one gold standard to screen heart rejection for patients that have heart transplantation. However, manual identification of heart rejection is expensive and time-consuming. With the development of imaging processing techniques and machine learning tools, automatic prediction of heart rejection using whole-slide images is one promising approach to improve the care of patients with heart transplants. In this paper, we first develop a histopathological whole-slide image processing pipeline to extract features automatically. Then, we construct deep neural networks with and without regularization and dropout to classify the patients into nonrejection and rejection respectively. Our results show that neural networks with regularization and dropout can significantly reduce overfitting and achieve more stable accuracies.
Identifiers
What OpenQuestion holds
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.