12 research outputs found
Multi-task Image Classification via Collaborative, Hierarchical Spike-and-Slab Priors
Promising results have been achieved in image classification problems by
exploiting the discriminative power of sparse representations for
classification (SRC). Recently, it has been shown that the use of
\emph{class-specific} spike-and-slab priors in conjunction with the
class-specific dictionaries from SRC is particularly effective in low training
scenarios. As a logical extension, we build on this framework for multitask
scenarios, wherein multiple representations of the same physical phenomena are
available. We experimentally demonstrate the benefits of mining joint
information from different camera views for multi-view face recognition.Comment: Accepted to International Conference in Image Processing (ICIP) 201
DFDL: Discriminative Feature-oriented Dictionary Learning for Histopathological Image Classification
In histopathological image analysis, feature extraction for classification is
a challenging task due to the diversity of histology features suitable for each
problem as well as presence of rich geometrical structure. In this paper, we
propose an automatic feature discovery framework for extracting discriminative
class-specific features and present a low-complexity method for classification
and disease grading in histopathology. Essentially, our Discriminative
Feature-oriented Dictionary Learning (DFDL) method learns class-specific
features which are suitable for representing samples from the same class while
are poorly capable of representing samples from other classes. Experiments on
three challenging real-world image databases: 1) histopathological images of
intraductal breast lesions, 2) mammalian lung images provided by the Animal
Diagnostics Lab (ADL) at Pennsylvania State University, and 3) brain tumor
images from The Cancer Genome Atlas (TCGA) database, show the significance of
DFDL model in a variety problems over state-of-the-art methodsComment: Accepted to IEEE International Symposium on Biomedical Imaging
(ISBI), 201
Adaptive Algorithm for Sparse Signal Recovery
Spike and slab priors play a key role in inducing sparsity for sparse signal
recovery. The use of such priors results in hard non-convex and mixed integer
programming problems. Most of the existing algorithms to solve the optimization
problems involve either simplifying assumptions, relaxations or high
computational expenses. We propose a new adaptive alternating direction method
of multipliers (AADMM) algorithm to directly solve the presented optimization
problem. The algorithm is based on the one-to-one mapping property of the
support and non-zero element of the signal. At each step of the algorithm, we
update the support by either adding an index to it or removing an index from it
and use the alternating direction method of multipliers to recover the signal
corresponding to the updated support. Experiments on synthetic data and
real-world images show that the proposed AADMM algorithm provides superior
performance and is computationally cheaper, compared to the recently developed
iterative convex refinement (ICR) algorithm
A hierarchical spike-and-slab model for pan-cancer survival using pan-omic data
Background: Pan-omics, pan-cancer analysis has advanced our understanding of the molecular heterogeneity of cancer. However, such analyses have been limited in their ability to use information from multiple sources of data (e.g., omics platforms) and multiple sample sets (e.g., cancer types) to predict clinical outcomes. We address the issue of prediction across multiple high-dimensional sources of data and sample sets by using molecular patterns identified by BIDIFAC+, a method for integrative dimension reduction of bidimensionally-linked matrices, in a Bayesian hierarchical model. Our model performs variable selection through spike-and-slab priors that borrow information across clustered data. We use this model to predict overall patient survival from the Cancer Genome Atlas with data from 29 cancer types and 4 omics sources and use simulations to characterize the performance of the hierarchical spike-and-slab prior. Results: We found that molecular patterns shared across all or most cancers were largely not predictive of survival. However, our model selected patterns unique to subsets of cancers that differentiate clinical tumor subtypes with markedly different survival outcomes. Some of these subtypes were previously established, such as subtypes of uterine corpus endometrial carcinoma, while others may be novel, such as subtypes within a set of kidney carcinomas. Through simulations, we found that the hierarchical spike-and-slab prior performs best in terms of variable selection accuracy and predictive power when borrowing information is advantageous, but also offers competitive performance when it is not. Conclusions: We address the issue of prediction across multiple sources of data by using results from BIDIFAC+ in a Bayesian hierarchical model for overall patient survival. By incorporating spike-and-slab priors that borrow information across cancers, we identified molecular patterns that distinguish clinical tumor subtypes within a single cancer and within a group of cancers. We also corroborate the flexibility and performance of using spike-and-slab priors as a Bayesian variable selection approach