18 research outputs found
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
Convolutional Neural Networks (CNNs) have been recently employed to solve
problems from both the computer vision and medical image analysis fields.
Despite their popularity, most approaches are only able to process 2D images
while most medical data used in clinical practice consists of 3D volumes. In
this work we propose an approach to 3D image segmentation based on a
volumetric, fully convolutional, neural network. Our CNN is trained end-to-end
on MRI volumes depicting prostate, and learns to predict segmentation for the
whole volume at once. We introduce a novel objective function, that we optimise
during training, based on Dice coefficient. In this way we can deal with
situations where there is a strong imbalance between the number of foreground
and background voxels. To cope with the limited number of annotated volumes
available for training, we augment the data applying random non-linear
transformations and histogram matching. We show in our experimental evaluation
that our approach achieves good performances on challenging test data while
requiring only a fraction of the processing time needed by other previous
methods
The Future of Digital Health with Federated Learning
Data-driven Machine Learning has emerged as a promising approach for building
accurate and robust statistical models from medical data, which is collected in
huge volumes by modern healthcare systems. Existing medical data is not fully
exploited by ML primarily because it sits in data silos and privacy concerns
restrict access to this data. However, without access to sufficient data, ML
will be prevented from reaching its full potential and, ultimately, from making
the transition from research to clinical practice. This paper considers key
factors contributing to this issue, explores how Federated Learning (FL) may
provide a solution for the future of digital health and highlights the
challenges and considerations that need to be addressed.Comment: This is a pre-print version of
https://www.nature.com/articles/s41746-020-00323-