163 research outputs found
Visual Feature Attribution using Wasserstein GANs
Attributing the pixels of an input image to a certain category is an
important and well-studied problem in computer vision, with applications
ranging from weakly supervised localisation to understanding hidden effects in
the data. In recent years, approaches based on interpreting a previously
trained neural network classifier have become the de facto state-of-the-art and
are commonly used on medical as well as natural image datasets. In this paper,
we discuss a limitation of these approaches which may lead to only a subset of
the category specific features being detected. To address this problem we
develop a novel feature attribution technique based on Wasserstein Generative
Adversarial Networks (WGAN), which does not suffer from this limitation. We
show that our proposed method performs substantially better than the
state-of-the-art for visual attribution on a synthetic dataset and on real 3D
neuroimaging data from patients with mild cognitive impairment (MCI) and
Alzheimer's disease (AD). For AD patients the method produces compellingly
realistic disease effect maps which are very close to the observed effects.Comment: Accepted to CVPR 201
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
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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