# Prior Attention Network for Multi-Lesion Segmentation in Medical Images

arXiv: https://arxiv.org/abs/2110.04735 · HTML: https://mlsanity.com/paper/2110.04735/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Oct 2021.

## Built on by (most cited first, 9 of 9)

- [A comprehensive survey on deep active learning in medical image analysis](https://mlsanity.com/paper/2310.14230/) (2023)
- [RCPS: Rectified Contrastive Pseudo Supervision for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2301.05500/) (2023)
- [Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective](https://mlsanity.com/paper/2506.10142/) (2025)
- [Towards Universal Text-driven CT Image Segmentation](https://mlsanity.com/paper/2503.06030/) (2025)
- [Diversity-Promoting Ensemble for Medical Image Segmentation](https://mlsanity.com/paper/2210.12388/) (2022)
- [CT evaluation of 2D and 3D holistic deep learning methods for the volumetric segmentation of airway lesions](https://mlsanity.com/paper/2403.08042/) (2024)
- [A Novel SLCA-UNet Architecture for Automatic MRI Brain Tumor Segmentation](https://mlsanity.com/paper/2307.08048/) (2023)
- [Selective Complementary Feature Fusion and Modal Feature Compression Interaction for Brain Tumor Segmentation](https://mlsanity.com/paper/2503.16149/) (2025)
- [ROBUST-WT: Robust Uncertainty-aware Segmentation Transform via Whitening and Training Enhancements](https://mlsanity.com/paper/2606.03069/) (2026)

## Abstract (arXiv)

The accurate segmentation of multiple types of lesions from adjacent tissues in medical images is significant in clinical practice. Convolutional neural networks (CNNs) based on the coarse-to-fine strategy have been widely used in this field. However, multi-lesion segmentation remains to be challenging due to the uncertainty in size, contrast, and high interclass similarity of tissues. In addition, the commonly adopted cascaded strategy is rather demanding in terms of hardware, which limits the potential of clinical deployment. To address the problems above,we propose a novel Prior Attention Network (PANet) that follows the coarse-to-fine strategy to perform multi-lesion segmentation in medical images. The proposed network achieves the two steps of segmentation in a single network by inserting lesion-related spatial attention mechanism in the network. Further, we also propose the intermediate supervision strategy for generating lesion-related attention to acquire the regions of interest (ROIs), which accelerates the convergence and obviously improves the segmentation performance. We have investigated the proposed segmentation framework in two applications: 2D segmentation of multiple lung infections in lung CT slices and 3D segmentation of multiple lesions in brain MRIs. Experimental results show that in both 2D and 3D segmentation tasks our proposed network achieves better performance with less computational cost compared with cascaded networks. The proposed network can be regarded as a universal solution to multi-lesion segmentation in both 2D and 3D tasks. The source code is available at: https://github.com/hsiangyuzhao/PANet.