Lymph Node Detection in T2 MRI with Transformers

Roth, H. R. e. a., “A new 2.5d representation for lymph node detection using random sets of deep convolutional neural network observations,” in [ MICCAI

2014

Earlier work this paper cites.

Seff, A. e. a., “Leveraging mid-level semantic boundary cues for automated lymph node detection,” in [ MICCAI

2015

Earlier work this paper cites.

Liu, J. and et al, “Mediastinal lymph node detection and station mapping on chest ct using spatial priors and random forest,” Medical Physics

2016

Earlier work this paper cites.

Amin, M. and et al., “ 8 t ​ h 8^{th} AJCC cancer staging manual: Continuing to build a bridge from a population-based to a more “personalized” approach to cancer staging,” CA: Canc. J. Clin

2017

Earlier work this paper cites.

Lin, T.-Y. and et al., “Focal loss for dense object detection,” in [ ICCV

2017

Earlier work this paper cites.

et al, O. D., “Lymph node detection in mr lymphography: false positive reduction using multi-view convolutional neural networks,” PeerJ

2019

Cited alongside, same era.

Tian, Z. and et al., “Fcos: Fully convolutional one-stage object detection,” in [ ICCV

2019

Cited alongside, same era.

Kong, T. and et al., “Foveabox: Beyond anchor-based object detector,” CoRR

Original

2019

Cited alongside, same era.

Chen, K. and et al., “MMDetection: Open mmlab detection toolbox and benchmark,” arXiv preprint arXiv:1906.07155

Original

2019

Cited alongside, same era.

Zhao, X. e. a., “Deep learning–based fully automated detection and segmentation of lymph nodes on multiparametric-mri for rectal cancer: A multicentre study,” EBioMedicine

2020

Carion, N. and et al., “End-to-end object detection with transformers,” CoRR

Original

2020

Later among the works it cites.

Peng, Y. and et al., “Automatic recognition of abdominal lymph nodes from clinical text,” in [ Clin. Nat. Lang. Proc

2020

Later among the works it cites.

Kociołek, M. e. a., “Does image normalization and intensity resolution impact texture classification?,” Computerized Medical Imaging and Graphics

2020

Later among the works it cites.

Zhang, H. and et al., “Varifocalnet: An iou-aware dense object detector,” in [ CVPR

2021

Closest in time.

Solovyev, R. and et al., “Weighted boxes fusion: Ensembling boxes from different object detection models,” Image and Vision Computing

2021

Closest in time.