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ULDor: A Universal Lesion Detector for CT Scans with Pseudo Masks and Hard Negative Example Mining [article]

Youbao Tang, Ke Yan, Yuxing Tang, Jiamin Liu, Jing Xiao, Ronald M. Summers
2019 arXiv   pre-print
Experimental results on the NIH DeepLesion dataset demonstrate that the ULDor is enhanced using pseudo masks and the proposed hard negative example mining strategy and achieves a sensitivity of 86.21%  ...  On the other hand, this work proposes a hard negative example mining strategy to reduce the false positives for improving the detection performance.  ...  This research was supported by the Intramural Research Program of the National Institutes of Health Clinical Center and by the Ping An Insurance Company through a Cooperative Research and Development Agreement  ... 
arXiv:1901.06359v1 fatcat:domsdwcbvjd23nkfaobbx3ceoy

Universal Lesion Detection by Learning from Multiple Heterogeneously Labeled Datasets [article]

Ke Yan, Jinzheng Cai, Adam P. Harrison, Dakai Jin, Jing Xiao, Le Lu
2020 arXiv   pre-print
First, we learn a multi-head multi-task lesion detector using all datasets and generate lesion proposals on DeepLesion.  ...  instances; and several fully-labeled single-type lesion datasets, such as LUNA for lung nodules and LiTS for liver tumors.  ...  ULDor [37] mined hard negative proposals with a trained detector to retrain the model, but the mined negatives may actually contain positives because of missing annotations.  ... 
arXiv:2005.13753v1 fatcat:cpsbd73ew5baxp2slt2hipha6q