2,046 research outputs found
NiftyNet: a deep-learning platform for medical imaging
Medical image analysis and computer-assisted intervention problems are
increasingly being addressed with deep-learning-based solutions. Established
deep-learning platforms are flexible but do not provide specific functionality
for medical image analysis and adapting them for this application requires
substantial implementation effort. Thus, there has been substantial duplication
of effort and incompatible infrastructure developed across many research
groups. This work presents the open-source NiftyNet platform for deep learning
in medical imaging. The ambition of NiftyNet is to accelerate and simplify the
development of these solutions, and to provide a common mechanism for
disseminating research outputs for the community to use, adapt and build upon.
NiftyNet provides a modular deep-learning pipeline for a range of medical
imaging applications including segmentation, regression, image generation and
representation learning applications. Components of the NiftyNet pipeline
including data loading, data augmentation, network architectures, loss
functions and evaluation metrics are tailored to, and take advantage of, the
idiosyncracies of medical image analysis and computer-assisted intervention.
NiftyNet is built on TensorFlow and supports TensorBoard visualization of 2D
and 3D images and computational graphs by default.
We present 3 illustrative medical image analysis applications built using
NiftyNet: (1) segmentation of multiple abdominal organs from computed
tomography; (2) image regression to predict computed tomography attenuation
maps from brain magnetic resonance images; and (3) generation of simulated
ultrasound images for specified anatomical poses.
NiftyNet enables researchers to rapidly develop and distribute deep learning
solutions for segmentation, regression, image generation and representation
learning applications, or extend the platform to new applications.Comment: Wenqi Li and Eli Gibson contributed equally to this work. M. Jorge
Cardoso and Tom Vercauteren contributed equally to this work. 26 pages, 6
figures; Update includes additional applications, updated author list and
formatting for journal submissio
Generalized Zero Shot Learning For Medical Image Classification
In many real world medical image classification settings we do not have
access to samples of all possible disease classes, while a robust system is
expected to give high performance in recognizing novel test data. We propose a
generalized zero shot learning (GZSL) method that uses self supervised learning
(SSL) for: 1) selecting anchor vectors of different disease classes; and 2)
training a feature generator. Our approach does not require class attribute
vectors which are available for natural images but not for medical images. SSL
ensures that the anchor vectors are representative of each class. SSL is also
used to generate synthetic features of unseen classes. Using a simpler
architecture, our method matches a state of the art SSL based GZSL method for
natural images and outperforms all methods for medical images. Our method is
adaptable enough to accommodate class attribute vectors when they are available
for natural images
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