IEEE Trans Med Imaging
July 2023
Dataset auditing for machine learning (ML) models is a method to evaluate if a given dataset is used in training a model. In a Federated Learning setting where multiple institutions collaboratively train a model with their decentralized private datasets, dataset auditing can facilitate the enforcement of regulations, which provide rules for preserving privacy, but also allow users to revoke authorizations and remove their data from collaboratively trained models. This paper first proposes a set of requirements for a practical dataset auditing method, and then present a novel dataset auditing method called Ensembled Membership Auditing ( EMA ).
View Article and Find Full Text PDFEmerging magnetic resonance (MR) guided radiotherapy affords significantly improved anatomy visualization and, subsequently, more effective personalized treatment. The new therapy paradigm imposes significant demands on radiation dose calculation quality and speed, creating an unmet need for the acceleration of Monte Carlo (MC) dose calculation. Existing deep learning approaches to denoise the final plan MC dose fail to achieve the accuracy and speed requirements of large-scale beamlet dose calculation in the presence of a strong magnetic field for online adaptive radiotherapy planning.
View Article and Find Full Text PDFIEEE Trans Med Imaging
August 2019
The segmentation of pancreas is important for medical image analysis, yet it faces great challenges of class imbalance, background distractions, and non-rigid geometrical features. To address these difficulties, we introduce a deep Q network (DQN) driven approach with deformable U-Net to accurately segment the pancreas by explicitly interacting with contextual information and extract anisotropic features from pancreas. The DQN-based model learns a context-adaptive localization policy to produce a visually tightened and precise localization bounding box of the pancreas.
View Article and Find Full Text PDF