30,917 research outputs found
An Algorithm for Learning Shape and Appearance Models without Annotations
This paper presents a framework for automatically learning shape and
appearance models for medical (and certain other) images. It is based on the
idea that having a more accurate shape and appearance model leads to more
accurate image registration, which in turn leads to a more accurate shape and
appearance model. This leads naturally to an iterative scheme, which is based
on a probabilistic generative model that is fit using Gauss-Newton updates
within an EM-like framework. It was developed with the aim of enabling
distributed privacy-preserving analysis of brain image data, such that shared
information (shape and appearance basis functions) may be passed across sites,
whereas latent variables that encode individual images remain secure within
each site. These latent variables are proposed as features for
privacy-preserving data mining applications.
The approach is demonstrated qualitatively on the KDEF dataset of 2D face
images, showing that it can align images that traditionally require shape and
appearance models trained using manually annotated data (manually defined
landmarks etc.). It is applied to MNIST dataset of handwritten digits to show
its potential for machine learning applications, particularly when training
data is limited. The model is able to handle ``missing data'', which allows it
to be cross-validated according to how well it can predict left-out voxels. The
suitability of the derived features for classifying individuals into patient
groups was assessed by applying it to a dataset of over 1,900 segmented
T1-weighted MR images, which included images from the COBRE and ABIDE datasets.Comment: 61 pages, 16 figures (some downsampled by a factor of 4), submitted
to MedI
On Lightweight Privacy-Preserving Collaborative Learning for IoT Objects
The Internet of Things (IoT) will be a main data generation infrastructure
for achieving better system intelligence. This paper considers the design and
implementation of a practical privacy-preserving collaborative learning scheme,
in which a curious learning coordinator trains a better machine learning model
based on the data samples contributed by a number of IoT objects, while the
confidentiality of the raw forms of the training data is protected against the
coordinator. Existing distributed machine learning and data encryption
approaches incur significant computation and communication overhead, rendering
them ill-suited for resource-constrained IoT objects. We study an approach that
applies independent Gaussian random projection at each IoT object to obfuscate
data and trains a deep neural network at the coordinator based on the projected
data from the IoT objects. This approach introduces light computation overhead
to the IoT objects and moves most workload to the coordinator that can have
sufficient computing resources. Although the independent projections performed
by the IoT objects address the potential collusion between the curious
coordinator and some compromised IoT objects, they significantly increase the
complexity of the projected data. In this paper, we leverage the superior
learning capability of deep learning in capturing sophisticated patterns to
maintain good learning performance. Extensive comparative evaluation shows that
this approach outperforms other lightweight approaches that apply additive
noisification for differential privacy and/or support vector machines for
learning in the applications with light data pattern complexities.Comment: 12 pages,IOTDI 201
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