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SPCXR: Self-supervised Pretraining using Chest X-rays Towards a Domain Specific Foundation Model
However, the traditional diagnostic tool design methods based on supervised learning are burdened by the need to provide training data annotation, which should be of good quality for better clinical outcomes.
Ranked #1 on
Semantic Segmentation
on Montgomery County X-ray Set
A Generic Approach to Lung Field Segmentation from Chest Radiographs using Deep Space and Shape Learning
The main contributions of our work are: (1) a generic lung field segmentation framework from CXR accommodating large shape variation for adult and pediatric cohorts; (2) a deep representation learning detection mechanism, \emph{ensemble space learning}, for robust object localization; and (3) \emph{marginal shape deep learning} for the shape deformation parameter estimation.
