Article... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics2018
Prediction of Heart Transplant Rejection Using Histopathological Whole-Slide Imaging.
Article in ... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis 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.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic review of generative adversarial networks (GANs) in cell microscopy: Trends, practices, and impact on image augmentation.PloS one · 2025Pooled it
- 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
- Optimization of deep learning models for the prediction of gene mutations using unsupervised clustering.The journal of pathology. Clinical research · 2023Article
- Application of Deep Learning in Histopathology Images of Breast Cancer: A Review.Micromachines · 2022Review
- Computational Analysis of Routine Biopsies Improves Diagnosis and Prediction of Cardiac Allograft Vasculopathy.Circulation · 2022Article
- An automated computational image analysis pipeline for histological grading of cardiac allograft rejection.European heart journal · 2021Article
- 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
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Endomyocardial biopsies are the current gold standard for monitoring heart transplant patients for signs of cardiac allograft rejection. Manually analyzing the acquired tissue samples can be costly, time-consuming, and subjective. Computer-aided diagnosis, using digitized whole-slide images, has been used to classify the presence and grading of diseases such as brain tumors and breast cancer, and we expect it can be used for prediction of cardiac allograft rejection. In this paper, we first create a pipeline to normalize and extract pixel-level and object-level features from histopathological whole-slide images of endomyocardial biopsies. Then, we develop a two-stage classification algorithm, where we first cluster individual tiles and then use the frequency of tiles in each cluster for classification of each whole-slide image. Our results show that the addition of an unsupervised clustering step leads to higher classification accuracy, as well as the importance of object-level features based on the pathophysiology of rejection. Future expansion of this study includes the development of a multiclass classification pipeline for subtypes and grades of cardiac allograft rejection.
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.