ArticleMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention2018
Subject2Vec: Generative-Discriminative Approach from a Set of Image Patches to a Vector.
Article in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed, 20 citations in OpenAlex.
- Emphysema progression risk in COPD using a localized foundational model of density evolution.NPJ digital medicine · 2025Article
- Novel Lobe-based Transformer model (LobTe) to predict emphysema progression in Alpha-1 Antitrypsin Deficiency.Computers in biology and medicine · 2025Article
- A Survey on Explainable Artificial Intelligence (XAI) Techniques for Visualizing Deep Learning Models in Medical Imaging.Journal of imaging · 2024Article
- 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
- Explainable medical imaging AI needs human-centered design: guidelines and evidence from a systematic review.NPJ digital medicine · 2022Review
- 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
- Knowledge Distillation via Constrained Variational Inference.Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial IntelligenceArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
Abstract
We propose an attention-based method that aggregates local image features to a subject-level representation for predicting disease severity. In contrast to classical deep learning that requires a fixed dimensional input, our method operates on a
Indexed as
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.