Effective Lymph Nodes Detection in CT Scans Using Location Debiased
  Query Selection and Contrastive Query Representation in Transformer
Qinji Yu Yirui Wang Ke Yan Haoshen Li Dazhou Guo Li Zhang Le Lu Na Shen Qifeng Wang Xiaowei Ding Xianghua Ye Dakai Jin
Lymph node (LN) assessment is a critical, indispensable 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 works typically yield 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, to achieve
more accurate performance. By enhancing the 2D backbone with a multi-scale 2.5D
feature fusion to incorporate 3D context explicitly, more importantly, we make
two main contributions to improve the representation quality of LN queries. 1)
Considering that LN boundaries are often unclear, an IoU prediction head and a
location debiased query selection are proposed to select LN queries of higher
localization accuracy as the decoder query's initialization. 2) To reduce FPs,
query contrastive learning is employed to explicitly reinforce LN queries
towards their best-matched ground-truth queries over unmatched query
predictions. 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 > 4-5% average recall at the
same FP rates in both internal and external testing. We further evaluate on the
universal lesion detection task using NIH DeepLesion benchmark, and our method
achieves the top performance of 88.46% averaged recall across 0.5 to 4 FPs per
image, compared with other leading reported results.