ArticleMedical image analysis2022
Global-Local attention network with multi-task uncertainty loss for abnormal lymph node detection in MR images.
Article in Medical image analysis, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed, 28 citations in OpenAlex.
- [Detection of neurofibroma combining radiomics and ensemble learning].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Article
- Multi-attention Mechanism for Enhanced Pseudo-3D Prostate Zonal Segmentation.Journal of imaging informatics in medicine · 2025Article
- Deep learning for malignant lymph node segmentation and detection: a review.Frontiers in immunology · 2025Review
- Segmentation of mediastinal lymph nodes in CT with anatomical priors.International journal of computer assisted radiology and surgery · 2024Article
- Detection of abdominopelvic lymph nodes in multi-parametric MRI.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2024Article
- Universal detection and segmentation of lymph nodes in multi-parametric MRI.International journal of computer assisted radiology and surgery · 2024Article
- Universal lymph node detection in T2 MRI using neural networks.International journal of computer assisted radiology and surgery · 2023Article
- COVID-19 diagnosis via chest X-ray image classification based on multiscale class residual attention.Computers in biology and medicine · 2022Article
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
Abstract
Accurate and reliable detection of abnormal lymph nodes in magnetic resonance (MR) images is very helpful for the diagnosis and treatment of numerous diseases. However, it is still a challenging task due to similar appearances between abnormal lymph nodes and other tissues. In this paper, we propose a novel network based on an improved Mask R-CNN framework for the detection of abnormal lymph nodes in MR images. Instead of laboriously collecting large-scale pixel-wise annotated training data, pseudo masks generated from RECIST bookmarks on hand are utilized as the supervision. Different from the standard Mask R-CNN architecture, there are two main innovations in our proposed network: 1) global-local attention which encodes the global and local scale context for detection and utilizes the channel attention mechanism to extract more discriminative features and 2) multi-task uncertainty loss which adaptively weights multiple objective loss functions based on the uncertainty of each task to automatically search the optimal solution. For the experiments, we built a new abnormal lymph node dataset with 821 RECIST bookmarks of 41 different types of abnormal abdominal lymph nodes from 584 different patients. The experimental results showed the superior performance of our algorithm over compared state-of-the-art approaches.
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