Recist-Net: Lesion Detection Via Grouping Keypoints On Recist-Based Annotation

@article{Xie2021RecistNetLD,
  title={Recist-Net: Lesion Detection Via Grouping Keypoints On Recist-Based Annotation},
  author={Cong Xie and Shilei Cao and Dong Wei and Hongyu Zhou and Kai Ma and Xianli Zhang and Buyue Qian and Liansheng Wang and Yefeng Zheng},
  journal={2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)},
  year={2021},
  pages={921-924}
}
  • Cong Xie, Shilei Cao, +6 authors Yefeng Zheng
  • Published 13 April 2021
  • Computer Science
  • 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)
Universal lesion detection in computed tomography (CT) images is an important yet challenging task due to the large variations in lesion type, size, shape, and appearance. Considering that data in clinical routine (such as the DeepLesion dataset) are usually annotated with a long and a short diameter according to the standard of Response Evaluation Criteria in Solid Tumors (RECIST) diameters, we propose RECIST-Net, a new approach to lesion detection in which the four extreme points and center… 
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