Effective Lymph Nodes Detection in CT Scans Using Location Debiased Query Selection and Contrastive Query Representation in Transformer | SpringerLink
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Effective Lymph Nodes Detection in CT Scans Using Location Debiased Query Selection and Contrastive Query Representation in Transformer

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Computer Vision – ECCV 2024 (ECCV 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15100))

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Abstract

Lymph node (LN) assessment is a critical yet very challenging task in the routine clinical workflow of radiology and oncology. Accurate LN analysis is essential for cancer diagnosis, staging and treatment planning. Finding scatteredly distributed, low-contrast clinically relevant LNs in 3D CT is difficult even for experienced physicians under high inter-observer variations. Previous automatic LN detection typically yields limited recall and high false positives (FPs) due to adjacent anatomies with similar image intensities, shapes or textures (vessels, muscles, esophagus, etc.). In this work, we propose a new LN DEtection TRansformer, named LN-DETR, with location debiased query selection and contrastive query learning to enhance the representation ability of LN queries, important to increase the detection sensitivity and reduce FPs or duplicates. We also enhance LN-DETR by adapting an efficient multi-scale 2.5D fusion scheme to incorporate the 3D context. Trained and tested on 3D CT scans of 1067 patients (with \(10,000+\) labeled LNs) via combining seven LN datasets from different body parts (neck, chest, and abdomen) and pathologies/cancers, our method significantly improves the performance of previous leading methods by >4\(\sim \)5% average recall at the same FP rates in both internal and external testing. We further evaluate on the universal lesion detection task using DeepLesion benchmark, and our method achieves the top performance of 88.46% averaged recall, compared with other leading reported results.

Q. Yu, Y. Wang—Equal contribution.

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Notes

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    https://github.com/CSCYQJ/ECCV24_LN_DETR.

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Authors and Affiliations

  1. Shanghai Jiao Tong University, Shanghai, China

    Qinji Yu & Xiaowei Ding

  2. DAMO Academy, Alibaba Group, Hangzhou, China

    Qinji Yu, Yirui Wang, Ke Yan, Dazhou Guo, Le Lu & Dakai Jin

  3. Hupan Lab, 310023, Hangzhou, China

    Ke Yan

  4. Peking University, Beijing, China

    Haoshen Li & Li Zhang

  5. Zhongshan Hospital Fudan University, Shanghai, China

    Na Shen

  6. Sichuan Cancer Hospital, Chengdu, China

    Qifeng Wang

  7. The First Affiliated Hospital Zhejiang University, Hangzhou, China

    Xianghua Ye

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  1. Qinji Yu
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  1. University of Birmingham, Birmingham, UK

    Aleš Leonardis

  2. University of Trento, Trento, Italy

    Elisa Ricci

  3. Technical University of Darmstadt, Darmstadt, Hessen, Germany

    Stefan Roth

  4. Princeton University, Palo Alto, CA, USA

    Olga Russakovsky

  5. Czech Technical University in Prague, Prague, Czech Republic

    Torsten Sattler

  6. École des Ponts ParisTech, Marne-la-Vallée, France

    Gül Varol

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Yu, Q. et al. (2025). Effective Lymph Nodes Detection in CT Scans Using Location Debiased Query Selection and Contrastive Query Representation in Transformer. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15100. Springer, Cham. https://doi.org/10.1007/978-3-031-72946-1_11

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