mlsanityData to 14 Oct 2026

UMedNeRF: Uncertainty-aware Single View Volumetric Rendering for Medical Neural Radiance Fields

arXiv 2311.05836 ↗First posted 10 Nov 2023eess.IV cs.CV cs.LGcited by 10

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Abstract (arXiv)

In the field of clinical medicine, computed tomography (CT) is an effective medical imaging modality for the diagnosis of various pathologies. Compared with X-ray images, CT images can provide more information, including multi-planar slices and three-dimensional structures for clinical diagnosis. However, CT imaging requires patients to be exposed to large doses of ionizing radiation for a long time, which may cause irreversible physical harm. In this paper, we propose an Uncertainty-aware MedNeRF (UMedNeRF) network based on generated radiation fields. The network can learn a continuous representation of CT projections from 2D X-ray images by obtaining the internal structure and depth information and using adaptive loss weights to ensure the quality of the generated images. Our model is trained on publicly available knee and chest datasets, and we show the results of CT projection rendering with a single X-ray and compare our method with other methods based on generated radiation fields.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 8 of its 19 references that are in the map. First posted on arXiv 10 Nov 2023.

Builds on

8 of 19 references.

References · 8

  1. 2020pixelNeRF: Neural Radiance Fields from One or Few Imagescited by 1,584
  2. 2017Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semanticscited by 1,358
  3. 2020GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fieldscited by 785
  4. 2020GRAF: Generative Radiance Fields for 3D-Aware Image Synthesiscited by 689
  5. 2020Uncertainty Aware Semi-Supervised Learning on Graph Datacited by 95
  6. 2022MedNeRF: Medical Neural Radiance Fields for Reconstructing 3D-aware CT-Projections from a Single X-raycited by 66
  7. 2020Learning by Minimizing the Sum of Ranked Rangecited by 27
  8. 2022Rank-based Decomposable Losses in Machine Learning: A Surveycited by 26

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 6; 2025: 3; 2026: 1.

10 papers.

  1. 2024LLM-MedQA: Enhancing Medical Question Answering through Case Studies in Large Language Modelscited by 32
  2. 2024UU-Mamba: Uncertainty-aware U-Mamba for Cardiac Image Segmentationcited by 15
  3. 2024Neural Radiance Field-based Visual Rendering: A Comprehensive Reviewcited by 12
  4. 2024Neural Radiance Fields in Medical Imaging: A Surveycited by 7
  5. 2024NeRF-US: Removing Ultrasound Imaging Artifacts from Neural Radiance Fields in the Wildcited by 7
  6. 2025Improving Generalization of Medical Image Registration Foundation Modelcited by 6
  7. 2025MedChat: A Multi-Agent Framework for Multimodal Diagnosis with Large Language Modelscited by 5
  8. 2025Robust Fairness Vision-Language Learning for Medical Image Analysiscited by 4
  9. 2024An Uncertainty-Aware Generalization Framework for Cardiovascular Image Segmentationcited by 3
  10. 2026Weight Group-wise Post-Training Quantization for Medical Foundation Modelcited by 0