# AWEU-Net: An Attention-Aware Weight Excitation U-Net for Lung Nodule Segmentation

arXiv: https://arxiv.org/abs/2110.05144 · HTML: https://mlsanity.com/paper/2110.05144/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 43 references that are in the map.
- First posted on arXiv 11 Oct 2021.

## Builds on (most cited first, 23 of 23)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [Dual Attention Network for Scene Segmentation](https://mlsanity.com/paper/1809.02983/) (2018)
- [Large Kernel Matters -- Improve Semantic Segmentation by Global Convolutional Network](https://mlsanity.com/paper/1703.02719/) (2017)
- [Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge](https://mlsanity.com/paper/1612.08012/) (2016)
- [Speed/accuracy trade-offs for modern convolutional object detectors](https://mlsanity.com/paper/1611.10012/) (2016)
- [Pyramid Attention Network for Semantic Segmentation](https://mlsanity.com/paper/1805.10180/) (2018)
- [Mish: A Self Regularized Non-Monotonic Activation Function](https://mlsanity.com/paper/1908.08681/) (2019)
- [NoduleNet: Decoupled False Positive Reductionfor Pulmonary Nodule Detection and Segmentation](https://mlsanity.com/paper/1907.11320/) (2019)
- [Joint Learning for Pulmonary Nodule Segmentation, Attributes and Malignancy Prediction](https://mlsanity.com/paper/1802.03584/) (2018)
- [Dual-branch residual network for lung nodule segmentation](https://mlsanity.com/paper/1905.08413/) (2019)
- [A Robust Adaptive Stochastic Gradient Method for Deep Learning](https://mlsanity.com/paper/1703.00788/) (2017)
- [iW-Net: an automatic and minimalistic interactive lung nodule segmentation deep network](https://mlsanity.com/paper/1811.12789/) (2018)
- [U-Det: A Modified U-Net architecture with bidirectional feature network for lung nodule segmentation](https://mlsanity.com/paper/2003.09293/) (2020)

## Built on by (most cited first, 1 of 1)

- [Bridging spatial awareness and global context in medical image segmentation](https://mlsanity.com/paper/2512.06560/) (2025)

## Abstract (arXiv)

Lung cancer is deadly cancer that causes millions of deaths every year around the world. Accurate lung nodule detection and segmentation in computed tomography (CT) images is the most important part of diagnosing lung cancer in the early stage. Most of the existing systems are semi-automated and need to manually select the lung and nodules regions to perform the segmentation task. To address these challenges, we proposed a fully automated end-to-end lung nodule detection and segmentation system based on a deep learning approach. In this paper, we used Optimized Faster R-CNN; a state-of-the-art detection model to detect the lung nodule regions in the CT scans. Furthermore, we proposed an attention-aware weight excitation U-Net, called AWEU-Net, for lung nodule segmentation and boundaries detection. To achieve more accurate nodule segmentation, in AWEU-Net, we proposed position attention-aware weight excitation (PAWE), and channel attention-aware weight excitation (CAWE) blocks to highlight the best aligned spatial and channel features in the input feature maps. The experimental results demonstrate that our proposed model yields a Dice score of 89.79% and 90.35%, and an intersection over union (IoU) of 82.34% and 83.21% on the publicly LUNA16 and LIDC-IDRI datasets, respectively.