ArticleProceedings of machine learning research2021
A New Semi-supervised Learning Benchmark for Classifying View and Diagnosing Aortic Stenosis from Echocardiograms.
Article in Proceedings of machine learning research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
12 citing papers in PubMed.
- Artificial Intelligence in Cardiovascular Ultrasound: Clinical Applications, Foundation Models, and the Path to Precision Cardiology.Journal of clinical medicine · 2026Review
- Echo-Vision-FM: a pre-training and fine-tuning framework for echocardiogram video vision foundation model.Nature communications · 2025Article
- On the public dissemination and open sourcing of ultrasound resources, datasets and deep learning models.NPJ digital medicine · 2025Article
- Core-Periphery Multi-Modality Feature Alignment for Zero-Shot Medical Image Analysis.IEEE transactions on medical imaging · 2025Article
- RAMAS-Net: a module-optimized convolutional network model for aortic valve stenosis recognition in echocardiography.Frontiers in medicine · 2025Article
- Efficient deep learning-based automated diagnosis from echocardiography with contrastive self-supervised learning.Communications medicine · 2024Article
- Atrial Septal Defect Detection in Children Based on Ultrasound Video Using Multiple Instances Learning.Journal of imaging informatics in medicine · 2024Article
- SPEMix: a lightweight method via superclass pseudo-label and efficient mixup for echocardiogram view classification.Frontiers in artificial intelligence · 2024Article
- A Probabilistic Method to Predict Classifier Accuracy on Larger Datasets given Small Pilot Data.Proceedings of machine learning research · 2023Article
- Severe aortic stenosis detection by deep learning applied to echocardiography.European heart journal · 2023Article
- Detecting Heart Disease from Multi-View Ultrasound Images via Supervised Attention Multiple Instance Learning.Proceedings of machine learning research · 2023Article
- Automated Detection of Aortic Stenosis Using Machine Learning.Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography · 2023Article
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
Semi-supervised image classification has shown substantial progress in learning from limited labeled data, but recent advances remain largely untested for clinical applications. Motivated by the urgent need to improve timely diagnosis of life-threatening heart conditions, especially aortic stenosis, we develop a benchmark dataset to assess semi-supervised approaches to two tasks relevant to cardiac ultrasound (echocardiogram) interpretation: view classification and disease severity classification. We find that a state-of-the-art method called MixMatch achieves promising gains in heldout accuracy on both tasks, learning from a large volume of truly unlabeled images as well as a labeled set collected at great expense to achieve better performance than is possible with the labeled set alone. We further pursue
Identifiers
39911283PMC11798330What 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.