ArticleInformation processing in medical imaging : proceedings of the ... conference2017
A Likelihood-Free Approach for Characterizing Heterogeneous Diseases in Large-Scale Studies.
Article in Information processing in medical imaging : proceedings of the ... conference, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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.
- DrasCLR: A self-supervised framework of learning disease-related and anatomy-specific representation for 3D lung CT images.Medical image analysis · 2024Article
- Development of machine learning model to predict pulmonary function with low-dose CT-derived parameter response mapping in a community-based chest screening cohort.Journal of applied clinical medical physics · 2023Article
- Unpaired data empowers association tests.Bioinformatics (Oxford, England) · 2021Article
- Improving clinical disease subtyping and future events prediction through a chest CT-based deep learning approach.Medical physics · 2021Article
- Latent traits of lung tissue patterns in former smokers derived by dual channel deep learning in computed tomography images.Scientific reports · 2021Article
- Context Matters: Graph-based Self-supervised Representation Learning for Medical Images.Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence · 2021Article
- Incorporating External Information in Tissue Subtyping: A Topic Modeling Approach.Proceedings of machine learning research · 2021Article
- Subject2Vec: Generative-Discriminative Approach from a Set of Image Patches to a Vector.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 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
We propose a non-parametric approach for characterizing heterogeneous diseases in large-scale studies. We target diseases where multiple types of pathology present simultaneously in each subject and a more severe disease manifests as a higher level of tissue destruction. For each subject, we model the
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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.