762 research outputs found
Informative sample generation using class aware generative adversarial networks for classification of chest Xrays
Training robust deep learning (DL) systems for disease detection from medical
images is challenging due to limited images covering different disease types
and severity. The problem is especially acute, where there is a severe class
imbalance. We propose an active learning (AL) framework to select most
informative samples for training our model using a Bayesian neural network.
Informative samples are then used within a novel class aware generative
adversarial network (CAGAN) to generate realistic chest xray images for data
augmentation by transferring characteristics from one class label to another.
Experiments show our proposed AL framework is able to achieve state-of-the-art
performance by using about of the full dataset, thus saving significant
time and effort over conventional methods
Monocular Object Instance Segmentation and Depth Ordering with CNNs
In this paper we tackle the problem of instance-level segmentation and depth
ordering from a single monocular image. Towards this goal, we take advantage of
convolutional neural nets and train them to directly predict instance-level
segmentations where the instance ID encodes the depth ordering within image
patches. To provide a coherent single explanation of an image we develop a
Markov random field which takes as input the predictions of convolutional
neural nets applied at overlapping patches of different resolutions, as well as
the output of a connected component algorithm. It aims to predict accurate
instance-level segmentation and depth ordering. We demonstrate the
effectiveness of our approach on the challenging KITTI benchmark and show good
performance on both tasks.Comment: International Conference on Computer Vision (ICCV), 201
Multiscale Fields of Patterns
We describe a framework for defining high-order image models that can be used
in a variety of applications. The approach involves modeling local patterns in
a multiscale representation of an image. Local properties of a coarsened image
reflect non-local properties of the original image. In the case of binary
images local properties are defined by the binary patterns observed over small
neighborhoods around each pixel. With the multiscale representation we capture
the frequency of patterns observed at different scales of resolution. This
framework leads to expressive priors that depend on a relatively small number
of parameters. For inference and learning we use an MCMC method for block
sampling with very large blocks. We evaluate the approach with two example
applications. One involves contour detection. The other involves binary
segmentation.Comment: In NIPS 201
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