mlsanityData to 7 Oct 2026

Lymph Node Detection in T2 MRI with Transformers

arXiv 2111.04885 ↗First posted 9 Nov 2021eess.IV cs.AI cs.CVcited by 12

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Abstract (arXiv)

Identification of lymph nodes (LN) in T2 Magnetic Resonance Imaging (MRI) is an important step performed by radiologists during the assessment of lymphoproliferative diseases. The size of the nodes play a crucial role in their staging, and radiologists sometimes use an additional contrast sequence such as diffusion weighted imaging (DWI) for confirmation. However, lymph nodes have diverse appearances in T2 MRI scans, making it tough to stage for metastasis. Furthermore, radiologists often miss smaller metastatic lymph nodes over the course of a busy day. To deal with these issues, we propose to use the DEtection TRansformer (DETR) network to localize suspicious metastatic lymph nodes for staging in challenging T2 MRI scans acquired by different scanners and exam protocols. False positives (FP) were reduced through a bounding box fusion technique, and a precision of 65.41% and sensitivity of 91.66% at 4 FP per image was achieved. To the best of our knowledge, our results improve upon the current state-of-the-art for lymph node detection in T2 MRI scans.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 6 of its 15 references that are in the map. First posted on arXiv 9 Nov 2021.

Builds on

6 of 15 references.

References · 6

  1. 2020End-to-End Object Detection with Transformerscited by 7,384
  2. 2019FCOS: Fully Convolutional One-Stage Object Detectioncited by 1,573
  3. 2019MMDetection: Open MMLab Detection Toolbox and Benchmarkcited by 1,379
  4. 2020VarifocalNet: An IoU-aware Dense Object Detectorcited by 230
  5. 2014A New 2.5D Representation for Lymph Node Detection using Random Sets of Deep Convolutional Neural Network Observationscited by 56
  6. 2019FoveaBox: Beyond Anchor-based Object Detectorcited by 37

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Cited by year

Citing papers per year: 2022: 4; 2023: 1; 2024: 5; 2025: 2.

12 papers.

  1. 2022Transformers in Medical Imaging: A Surveycited by 245
  2. 2022Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectivescited by 32
  3. 2022A survey on attention mechanisms for medical applications: are we moving towards better algorithms?cited by 15
  4. 2024MRISegmentator-Abdomen: A Fully Automated Multi-Organ and Structure Segmentation Tool for T1-weighted Abdominal MRIcited by 7
  5. 2023Revisiting Computer-Aided Tuberculosis Diagnosiscited by 6
  6. 2022Universal Lymph Node Detection in T2 MRI using Neural Networkscited by 5
  7. 2024Segmentation of Mediastinal Lymph Nodes in CT with Anatomical Priorscited by 3
  8. 2024Understanding differences in applying DETR to natural and medical imagescited by 2
  9. 2024Detection Transformer for Teeth Detection, Segmentation, and Numbering in Oral Rare Diseases: Focus on Data Augmentation and Inpainting Techniquescited by 1
  10. 2024ASteISR: Adapting Single Image Super-resolution Pre-trained Model for Efficient Stereo Image Super-resolutioncited by 0
  11. 2025Universal Lymph Node Detection in Multiparametric MRI with Selective Augmentationcited by 0
  12. 2025Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentationcited by 0