# Feature-enhanced Adversarial Semi-supervised Semantic Segmentation Network for Pulmonary Embolism Annotation

arXiv: https://arxiv.org/abs/2204.04217 · HTML: https://mlsanity.com/paper/2204.04217/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 18 references that are in the map.
- First posted on arXiv 8 Apr 2022.

## Builds on (most cited first, 8 of 8)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [Adversarial Learning for Semi-Supervised Semantic Segmentation](https://mlsanity.com/paper/1802.07934/) (2018)
- [A review of domain adaptation without target labels](https://mlsanity.com/paper/1901.05335/) (2019)
- [CARAFE: Content-Aware ReAssembly of FEatures](https://mlsanity.com/paper/1905.02188/) (2019)
- [Rethinking Distributional Matching Based Domain Adaptation](https://mlsanity.com/paper/2006.13352/) (2020)

## Built on by (most cited first, 2 of 2)

- [Rethinking Pulmonary Embolism Segmentation: A Study of Current Approaches and Challenges with an Open Weight Model](https://mlsanity.com/paper/2509.18308/) (2025)
- [Unified-protocol voxel-level pulmonary embolism annotations for three public CT angiography datasets](https://mlsanity.com/paper/2608.24486/) (2026)

## Abstract (arXiv)

This study established a feature-enhanced adversarial semi-supervised semantic segmentation model to automatically annotate pulmonary embolism lesion areas in computed tomography pulmonary angiogram (CTPA) images. In current studies, all of the PE CTPA image segmentation methods are trained by supervised learning. However, the supervised learning models need to be retrained and the images need to be relabeled when the CTPA images come from different hospitals. This study proposed a semi-supervised learning method to make the model applicable to different datasets by adding a small amount of unlabeled images. By training the model with both labeled and unlabeled images, the accuracy of unlabeled images can be improved and the labeling cost can be reduced. Our semi-supervised segmentation model includes a segmentation network and a discriminator network. We added feature information generated from the encoder of segmentation network to the discriminator so that it can learn the similarity between predicted mask and ground truth mask. This HRNet-based architecture can maintain a higher resolution for convolutional operations so the prediction of small PE lesion areas can be improved. We used the labeled open-source dataset and the unlabeled National Cheng Kung University Hospital (NCKUH) (IRB number: B-ER-108-380) dataset to train the semi-supervised learning model, and the resulting mean intersection over union (mIOU), dice score, and sensitivity achieved 0.3510, 0.4854, and 0.4253, respectively on the NCKUH dataset. Then, we fine-tuned and tested the model with a small amount of unlabeled PE CTPA images from China Medical University Hospital (CMUH) (IRB number: CMUH110-REC3-173) dataset. Comparing the results of our semi-supervised model with the supervised model, the mIOU, dice score, and sensitivity improved from 0.2344, 0.3325, and 0.3151 to 0.3721, 0.5113, and 0.4967, respectively.