mlsanityData to 7 Oct 2026

Uncertainty-Guided Lung Nodule Segmentation with Feature-Aware Attention

arXiv 2110.12372 ↗First posted 24 Oct 2021eess.IV cs.CVcited by 6

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Abstract (arXiv)

Since radiologists have different training and clinical experiences, they may provide various segmentation annotations for a lung nodule. Conventional studies choose a single annotation as the learning target by default, but they waste valuable information of consensus or disagreements ingrained in the multiple annotations. This paper proposes an Uncertainty-Guided Segmentation Network (UGS-Net), which learns the rich visual features from the regions that may cause segmentation uncertainty and contributes to a better segmentation result. With an Uncertainty-Aware Module, this network can provide a Multi-Confidence Mask (MCM), pointing out regions with different segmentation uncertainty levels. Moreover, this paper introduces a Feature-Aware Attention Module to enhance the learning of the nodule boundary and density differences. Experimental results show that our method can predict the nodule regions with different uncertainty levels and achieve superior performance in LIDC-IDRI dataset.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 16 of its 30 references that are in the map. First posted on arXiv 24 Oct 2021.

Builds on

16 of 30 references.

References · 16

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
  4. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 4,993
  5. 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,518
  6. 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,420
  7. 2018A Probabilistic U-Net for Segmentation of Ambiguous Imagescited by 281
  8. 2020AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem?cited by 222
  9. 2018Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentationcited by 180
  10. 2019A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguitiescited by 65
  11. 2017Gabor Convolutional Networkscited by 55
  12. 2019Supervised Uncertainty Quantification for Segmentation with Multiple Annotationscited by 42
4 more
  1. 2018DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classificationcited by 36
  2. 2019NoduleNet: Decoupled False Positive Reductionfor Pulmonary Nodule Detection and Segmentationcited by 21
  3. 2020Channel-Attention Dense U-Net for Multichannel Speech Enhancementcited by 18
  4. 2019Dual-branch residual network for lung nodule segmentationcited by 7

Built on by

Cited by year

Citing papers per year: 2023: 3; 2024: 2; 2025: 1.

6 papers.

  1. 2025Uncertainty Quantification for Machine Learning in Healthcare: A Surveycited by 11
  2. 2023Lung Nodule Segmentation and Uncertain Region Prediction with an Uncertainty-Aware Attention Mechanismcited by 3
  3. 2023Uncertainty Driven Bottleneck Attention U-net for Organ at Risk Segmentationcited by 1
  4. 2023Elongated Physiological Structure Segmentation via Spatial and Scale Uncertainty-aware Networkcited by 0
  5. 2024Expert-Adaptive Medical Image Segmentationcited by 0
  6. 2024S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentationcited by 0