# Universal Lymph Node Detection in T2 MRI using Neural Networks

arXiv: https://arxiv.org/abs/2204.00622 · HTML: https://mlsanity.com/paper/2204.00622/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 22 references that are in the map.
- First posted on arXiv 31 Mar 2022.

## Builds on (most cited first, 9 of 9)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [FCOS: Fully Convolutional One-Stage Object Detection](https://mlsanity.com/paper/1904.01355/) (2019)
- [MMDetection: Open MMLab Detection Toolbox and Benchmark](https://mlsanity.com/paper/1906.07155/) (2019)
- [Soft-NMS -- Improving Object Detection With One Line of Code](https://mlsanity.com/paper/1704.04503/) (2017)
- [VarifocalNet: An IoU-aware Dense Object Detector](https://mlsanity.com/paper/2008.13367/) (2020)
- [ULDor: A Universal Lesion Detector for CT Scans with Pseudo Masks and Hard Negative Example Mining](https://mlsanity.com/paper/1901.06359/) (2019)
- [Lymph Node Detection in T2 MRI with Transformers](https://mlsanity.com/paper/2111.04885/) (2021)

## Built on by (most cited first, 5 of 5)

- [Segmentation of Mediastinal Lymph Nodes in CT with Anatomical Priors](https://mlsanity.com/paper/2401.06272/) (2024)
- [Biomedical Image Segmentation: A Systematic Literature Review of Deep Learning Based Object Detection Methods](https://mlsanity.com/paper/2408.03393/) (2024)
- [Universal Lymph Node Detection in Multiparametric MRI with Selective Augmentation](https://mlsanity.com/paper/2504.05196/) (2025)
- [Classification of Multi-Parametric Body MRI Series Using Deep Learning](https://mlsanity.com/paper/2506.15182/) (2025)
- [Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation](https://mlsanity.com/paper/2507.17971/) (2025)

## Abstract (arXiv)

Purpose: Identification of abdominal Lymph Nodes (LN) that are suspicious for metastasis in T2 Magnetic Resonance Imaging (MRI) scans is critical for staging of lymphoproliferative diseases. Prior work on LN detection has been limited to specific anatomical regions of the body (pelvis, rectum) in single MR slices. Therefore, the development of a universal approach to detect LN in full T2 MRI volumes is highly desirable. Methods: In this study, a Computer Aided Detection (CAD) pipeline to universally identify abdominal LN in volumetric T2 MRI using neural networks is proposed. First, we trained various neural network models for detecting LN: Faster RCNN with and without Hard Negative Example Mining (HNEM), FCOS, FoveaBox, VFNet, and Detection Transformer (DETR). Next, we show that the state-of-the-art (SOTA) VFNet model with Adaptive Training Sample Selection (ATSS) outperforms Faster RCNN with HNEM. Finally, we ensembled models that surpassed a 45% mAP threshold. We found that the VFNet model and one-stage model ensemble can be interchangeably used in the CAD pipeline. Results: Experiments on 122 test T2 MRI volumes revealed that VFNet achieved a 51.1% mAP and 78.7% recall at 4 false positives (FP) per volume, while the one-stage model ensemble achieved a mAP of 52.3% and sensitivity of 78.7% at 4FP. Conclusion: Our contribution is a CAD pipeline that detects LN in T2 MRI volumes, resulting in a sensitivity improvement of ~14 points over the current SOTA method for LN detection (sensitivity of 78.7% at 4 FP vs. 64.6% at 5 FP per volume).