ArticleOncotarget2025
Deep learning-based uncertainty quantification for quality assurance in hepatobiliary imaging-based techniques.
Article in Oncotarget, 2025. 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
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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
5 citing papers in PubMed.
- Review
- Decoding Uncertainty Quantification for Oncology-An Illustration Using Radiomics.Diagnostics (Basel, Switzerland) · 2026Article
- Leveraging Multimodal Foundation Models in Biliary Tract Cancer Research.Tomography (Ann Arbor, Mich.) · 2025Review
- Beyond Post hoc Explanations: A Comprehensive Framework for Accountable AI in Medical Imaging Through Transparency, Interpretability, and Explainability.Bioengineering (Basel, Switzerland) · 2025Review
- Beyond the Black Box: Explainable AI Transforms Hepatocellular Carcinoma Diagnosis at MRI.Radiology. Imaging cancer · 2025Article
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent advances in deep learning models have transformed medical imaging analysis, particularly in radiology. This editorial outlines how uncertainty quantification through embedding-based approaches enhances diagnostic accuracy and reliability in hepatobiliary imaging, with a specific focus on oncological conditions and early detection of precancerous lesions. We explore modern architectures like the Anisotropic Hybrid Network (AHUNet), which leverages both 2D imaging and 3D volumetric data through innovative convolutional approaches. We consider the implications for quality assurance in radiological practice and discuss recent clinical applications.
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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.