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XLSor: A Robust and Accurate Lung Segmentor on Chest X-Rays Using Criss-Cross Attention and Customized Radiorealistic Abnormalities Generation [article]

Youbao Tang, Yuxing Tang, Jing Xiao, Ronald M. Summers
2019 arXiv   pre-print
It consists of two key contributions, a criss-cross attention based segmentation network and radiorealistic chest X-ray image synthesis (i.e. a synthesized radiograph that appears anatomically realistic  ...  This paper proposes a novel framework for lung segmentation in chest X-rays.  ...  Acknowledgments 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  ... 
arXiv:1904.09229v1 fatcat:g5sg4omchjbofewktslqkovrke

PMED-Net: Pyramid Based Multi-Scale Encoder-Decoder Network for Medical Image Segmentation

Abbas Khan, Hyongsuk Kim, Leon Chua
2021 IEEE Access  
, brain tumor dataset, nuclei dataset, and X-ray dataset.  ...  Different variants of encoder-decoder networks are in practice for segmenting the medical images and U-Net is the most widely used one.  ...  One hundred samples have been taken from the NIH Chest X-ray dataset and annotated manually by [54] having various lung diseases.  ... 
doi:10.1109/access.2021.3071754 fatcat:5bno4scjw5d4jewnhuf3sycaf4