15,919 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
Understanding Anatomy Classification Through Attentive Response Maps
One of the main challenges for broad adoption of deep learning based models
such as convolutional neural networks (CNN), is the lack of understanding of
their decisions. In many applications, a simpler, less capable model that can
be easily understood is favorable to a black-box model that has superior
performance. In this paper, we present an approach for designing CNNs based on
visualization of the internal activations of the model. We visualize the
model's response through attentive response maps obtained using a fractional
stride convolution technique and compare the results with known imaging
landmarks from the medical literature. We show that sufficiently deep and
capable models can be successfully trained to use the same medical landmarks a
human expert would use. Our approach allows for communicating the model
decision process well, but also offers insight towards detecting biases.Comment: Accepted at ISBI, 201
Sketchy rendering for information visualization
We present and evaluate a framework for constructing sketchy style information visualizations that mimic data graphics drawn by hand. We provide an alternative renderer for the Processing graphics environment that redefines core drawing primitives including line, polygon and ellipse rendering. These primitives allow higher-level graphical features such as bar charts, line charts, treemaps and node-link diagrams to be drawn in a sketchy style with a specified degree of sketchiness. The framework is designed to be easily integrated into existing visualization implementations with minimal programming modification or design effort. We show examples of use for statistical graphics, conveying spatial imprecision and for enhancing aesthetic and narrative qualities of visual- ization. We evaluate user perception of sketchiness of areal features through a series of stimulus-response tests in order to assess users’ ability to place sketchiness on a ratio scale, and to estimate area. Results suggest relative area judgment is compromised by sketchy rendering and that its influence is dependent on the shape being rendered. They show that degree of sketchiness may be judged on an ordinal scale but that its judgement varies strongly between individuals. We evaluate higher-level impacts of sketchiness through user testing of scenarios that encourage user engagement with data visualization and willingness to critique visualization de- sign. Results suggest that where a visualization is clearly sketchy, engagement may be increased and that attitudes to participating in visualization annotation are more positive. The results of our work have implications for effective information visualization design that go beyond the traditional role of sketching as a tool for prototyping or its use for an indication of general uncertainty
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