ArticlePhysics in medicine and biology2023
Head and neck tumor segmentation convolutional neural network robust to missing PET/CT modalities using channel dropout.
Article in Physics in medicine and biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- Convolutional neural network analysis of cervical CT images: classification of Kikuchi disease, lymphoma, lymphadenitis, and tuberculosis.Biomedical engineering online · 2026Article
- Article
- Multi-modal segmentation with missing image data for automatic delineation of gross tumor volumes in head and neck cancers.Medical physics · 2024Article
- Applications and challenges of neural networks in otolaryngology (Review).Biomedical reports · 2024Review
- A Combined Loss-driven Framework for Automated Parotid Segmentation in Head-and-Neck Computed Tomography.Journal of medical physicsArticle
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
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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.