39,014 research outputs found
Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis
Machine learning (ML) algorithms have made a tremendous impact in the field
of medical imaging. While medical imaging datasets have been growing in size, a
challenge for supervised ML algorithms that is frequently mentioned is the lack
of annotated data. As a result, various methods which can learn with less/other
types of supervision, have been proposed. We review semi-supervised, multiple
instance, and transfer learning in medical imaging, both in diagnosis/detection
or segmentation tasks. We also discuss connections between these learning
scenarios, and opportunities for future research.Comment: Submitted to Medical Image Analysi
Hand Pose Estimation through Semi-Supervised and Weakly-Supervised Learning
We propose a method for hand pose estimation based on a deep regressor
trained on two different kinds of input. Raw depth data is fused with an
intermediate representation in the form of a segmentation of the hand into
parts. This intermediate representation contains important topological
information and provides useful cues for reasoning about joint locations. The
mapping from raw depth to segmentation maps is learned in a
semi/weakly-supervised way from two different datasets: (i) a synthetic dataset
created through a rendering pipeline including densely labeled ground truth
(pixelwise segmentations); and (ii) a dataset with real images for which ground
truth joint positions are available, but not dense segmentations. Loss for
training on real images is generated from a patch-wise restoration process,
which aligns tentative segmentation maps with a large dictionary of synthetic
poses. The underlying premise is that the domain shift between synthetic and
real data is smaller in the intermediate representation, where labels carry
geometric and topological meaning, than in the raw input domain. Experiments on
the NYU dataset show that the proposed training method decreases error on
joints over direct regression of joints from depth data by 15.7%.Comment: 13 pages, 10 figures, 4 table
Unsupervised High-level Feature Learning by Ensemble Projection for Semi-supervised Image Classification and Image Clustering
This paper investigates the problem of image classification with limited or
no annotations, but abundant unlabeled data. The setting exists in many tasks
such as semi-supervised image classification, image clustering, and image
retrieval. Unlike previous methods, which develop or learn sophisticated
regularizers for classifiers, our method learns a new image representation by
exploiting the distribution patterns of all available data for the task at
hand. Particularly, a rich set of visual prototypes are sampled from all
available data, and are taken as surrogate classes to train discriminative
classifiers; images are projected via the classifiers; the projected values,
similarities to the prototypes, are stacked to build the new feature vector.
The training set is noisy. Hence, in the spirit of ensemble learning we create
a set of such training sets which are all diverse, leading to diverse
classifiers. The method is dubbed Ensemble Projection (EP). EP captures not
only the characteristics of individual images, but also the relationships among
images. It is conceptually simple and computationally efficient, yet effective
and flexible. Experiments on eight standard datasets show that: (1) EP
outperforms previous methods for semi-supervised image classification; (2) EP
produces promising results for self-taught image classification, where
unlabeled samples are a random collection of images rather than being from the
same distribution as the labeled ones; and (3) EP improves over the original
features for image clustering. The code of the method is available on the
project page.Comment: 22 pages, 8 figure
Semi-supervised Spectral Clustering for Classification
We propose a Classification Via Clustering (CVC) algorithm which enables
existing clustering methods to be efficiently employed in classification
problems. In CVC, training and test data are co-clustered and class-cluster
distributions are used to find the label of the test data. To determine an
efficient number of clusters, a Semi-supervised Hierarchical Clustering (SHC)
algorithm is proposed. Clusters are obtained by hierarchically applying two-way
NCut by using signs of the Fiedler vector of the normalized graph Laplacian. To
this end, a Direct Fiedler Vector Computation algorithm is proposed. The graph
cut is based on the data structure and does not consider labels. Labels are
used only to define the stopping criterion for graph cut. We propose clustering
to be performed on the Grassmannian manifolds facilitating the formation of
spectral ensembles. The proposed algorithm outperformed state-of-the-art
image-set classification algorithms on five standard datasets
Smooth Sparse Coding via Marginal Regression for Learning Sparse Representations
We propose and analyze a novel framework for learning sparse representations,
based on two statistical techniques: kernel smoothing and marginal regression.
The proposed approach provides a flexible framework for incorporating feature
similarity or temporal information present in data sets, via non-parametric
kernel smoothing. We provide generalization bounds for dictionary learning
using smooth sparse coding and show how the sample complexity depends on the L1
norm of kernel function used. Furthermore, we propose using marginal regression
for obtaining sparse codes, which significantly improves the speed and allows
one to scale to large dictionary sizes easily. We demonstrate the advantages of
the proposed approach, both in terms of accuracy and speed by extensive
experimentation on several real data sets. In addition, we demonstrate how the
proposed approach could be used for improving semi-supervised sparse coding
Manifold regularization with GANs for semi-supervised learning
Generative Adversarial Networks are powerful generative models that are able
to model the manifold of natural images. We leverage this property to perform
manifold regularization by approximating a variant of the Laplacian norm using
a Monte Carlo approximation that is easily computed with the GAN. When
incorporated into the semi-supervised feature-matching GAN we achieve
state-of-the-art results for GAN-based semi-supervised learning on CIFAR-10 and
SVHN benchmarks, with a method that is significantly easier to implement than
competing methods. We also find that manifold regularization improves the
quality of generated images, and is affected by the quality of the GAN used to
approximate the regularizer
Semi-supervised structured output prediction by local linear regression and sub-gradient descent
We propose a novel semi-supervised structured output prediction method based
on local linear regression in this paper. The existing semi-supervise
structured output prediction methods learn a global predictor for all the data
points in a data set, which ignores the differences of local distributions of
the data set, and the effects to the structured output prediction. To solve
this problem, we propose to learn the missing structured outputs and local
predictors for neighborhoods of different data points jointly. Using the local
linear regression strategy, in the neighborhood of each data point, we propose
to learn a local linear predictor by minimizing both the complexity of the
predictor and the upper bound of the structured prediction loss. The
minimization problem is solved by sub-gradient descent algorithms. We conduct
experiments over two benchmark data sets, and the results show the advantages
of the proposed method.Comment: arXiv admin note: substantial text overlap with arXiv:1604.0301
Semi-Supervised Multitask Learning on Multispectral Satellite Images Using Wasserstein Generative Adversarial Networks (GANs) for Predicting Poverty
Obtaining reliable data describing local poverty metrics at a granularity
that is informative to policy-makers requires expensive and logistically
difficult surveys, particularly in the developing world. Not surprisingly, the
poverty stricken regions are also the ones which have a high probability of
being a war zone, have poor infrastructure and sometimes have governments that
do not cooperate with internationally funded development efforts. We train a
CNN on free and publicly available daytime satellite images of the African
continent from Landsat 7 to build a model for predicting local economic
livelihoods. Only 5% of the satellite images can be associated with labels
(which are obtained from DHS Surveys) and thus a semi-supervised approach using
a GAN (similar to the approach of Salimans, et al. (2016)), albeit with a more
stable-to-train flavor of GANs called the Wasserstein GAN regularized with
gradient penalty(Gulrajani, et al. (2017)) is used. The method of multitask
learning is employed to regularize the network and also create an end-to-end
model for the prediction of multiple poverty metrics.Comment: This project was recognized as the best two-person project during the
Spring 2017 offering of CS 231N Convolutional Neural Networks for Visual
Recognition. Second revised version corrects typographical errors and adds a
few additional reference
Trace Quotient with Sparsity Priors for Learning Low Dimensional Image Representations
This work studies the problem of learning appropriate low dimensional image
representations. We propose a generic algorithmic framework, which leverages
two classic representation learning paradigms, i.e., sparse representation and
the trace quotient criterion. The former is a well-known powerful tool to
identify underlying self-explanatory factors of data, while the latter is known
for disentangling underlying low dimensional discriminative factors in data.
Our developed solutions disentangle sparse representations of images by
employing the trace quotient criterion. We construct a unified cost function,
coined as the SPARse LOW dimensional representation (SparLow) function, for
jointly learning both a sparsifying dictionary and a dimensionality reduction
transformation. The SparLow function is widely applicable for developing
various algorithms in three classic machine learning scenarios, namely,
unsupervised, supervised, and semi-supervised learning. In order to develop
efficient joint learning algorithms for maximizing the SparLow function, we
deploy a framework of sparse coding with appropriate convex priors to ensure
the sparse representations to be locally differentiable. Moreover, we develop
an efficient geometric conjugate gradient algorithm to maximize the SparLow
function on its underlying Riemannian manifold. Performance of the proposed
SparLow algorithmic framework is investigated on several image processing
tasks, such as 3D data visualization, face/digit recognition, and object/scene
categorization.Comment: 17 page
Machine learning of neuroimaging to diagnose cognitive impairment and dementia: a systematic review and comparative analysis
INTRODUCTION: Advanced machine learning methods might help to identify
dementia risk from neuroimaging, but their accuracy to date is unclear.
METHODS: We systematically reviewed the literature, 2006 to late 2016, for
machine learning studies differentiating healthy ageing through to dementia of
various types, assessing study quality, and comparing accuracy at different
disease boundaries.
RESULTS: Of 111 relevant studies, most assessed Alzheimer's disease (AD) vs
healthy controls, used ADNI data, support vector machines and only T1-weighted
sequences. Accuracy was highest for differentiating AD from healthy controls,
and poor for differentiating healthy controls vs MCI vs AD, or MCI converters
vs non-converters. Accuracy increased using combined data types, but not by
data source, sample size or machine learning method.
DISCUSSION: Machine learning does not differentiate clinically-relevant
disease categories yet. More diverse datasets, combinations of different types
of data, and close clinical integration of machine learning would help to
advance the field
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