2,153 research outputs found
ALEC: Active learning with ensemble of classifiers for clinical diagnosis of coronary artery disease
Invasive angiography is the reference standard for coronary artery disease (CAD) diagnosis but is expensive and
associated with certain risks. Machine learning (ML) using clinical and noninvasive imaging parameters can be
used for CAD diagnosis to avoid the side effects and cost of angiography. However, ML methods require labeled
samples for efficient training. The labeled data scarcity and high labeling costs can be mitigated by active
learning. This is achieved through selective query of challenging samples for labeling. To the best of our
knowledge, active learning has not been used for CAD diagnosis yet. An Active Learning with Ensemble of
Classifiers (ALEC) method is proposed for CAD diagnosis, consisting of four classifiers. Three of these classifiers
determine whether a patient’s three main coronary arteries are stenotic or not. The fourth classifier predicts
whether the patient has CAD or not. ALEC is first trained using labeled samples. For each unlabeled sample, if the
outputs of the classifiers are consistent, the sample along with its predicted label is added to the pool of labeled
samples. Inconsistent samples are manually labeled by medical experts before being added to the pool. The
training is performed once more using the samples labeled so far. The interleaved phases of labeling and training
are repeated until all samples are labeled. Compared with 19 other active learning algorithms, ALEC combined
with a support vector machine classifier attained superior performance with 97.01% accuracy. Our method is
justified mathematically as well. We also comprehensively analyze the CAD dataset used in this paper. As part of
dataset analysis, features pairwise correlation is computed. The top 15 features contributing to CAD and stenosis
of the three main coronary arteries are determined. The relationship between stenosis of the main arteries is
presented using conditional probabilities. The effect of considering the number of stenotic arteries on sample
discrimination is investigated. The discrimination power over dataset samples is visualized, assuming each of the
three main coronary arteries as a sample label and considering the two remaining arteries as sample features
Histopathological image analysis : a review
Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe
Digital Image Access & Retrieval
The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio
On discriminative semi-supervised incremental learning with a multi-view perspective for image concept modeling
This dissertation presents the development of a semi-supervised incremental learning framework with a multi-view perspective for image concept modeling. For reliable image concept characterization, having a large number of labeled images is crucial. However, the size of the training set is often limited due to the cost required for generating concept labels associated with objects in a large quantity of images. To address this issue, in this research, we propose to incrementally incorporate unlabeled samples into a learning process to enhance concept models originally learned with a small number of labeled samples. To tackle the sub-optimality problem of conventional techniques, the proposed incremental learning framework selects unlabeled samples based on an expected error reduction function that measures contributions of the unlabeled samples based on their ability to increase the modeling accuracy. To improve the convergence property of the proposed incremental learning framework, we further propose a multi-view learning approach that makes use of multiple features such as color, texture, etc., of images when including unlabeled samples. For robustness to mismatches between training and testing conditions, a discriminative learning algorithm, namely a kernelized maximal- figure-of-merit (kMFoM) learning approach is also developed. Combining individual techniques, we conduct a set of experiments on various image concept modeling problems, such as handwritten digit recognition, object recognition, and image spam detection to highlight the effectiveness of the proposed framework.PhDCommittee Chair: Lee, Chin-Hui; Committee Member: Clements, Mark; Committee Member: Lee, Hsien-Hsin; Committee Member: McClellan, James; Committee Member: Yuan, Min
Bridging semantic gap: learning and integrating semantics for content-based retrieval
Digital cameras have entered ordinary homes and produced^incredibly large number
of photos. As a typical example of broad image domain, unconstrained consumer
photos vary significantly. Unlike professional or domain-specific images, the objects
in the photos are ill-posed, occluded, and cluttered with poor lighting, focus, and
exposure. Content-based image retrieval research has yet to bridge the semantic gap
between computable low-level information and high-level user interpretation.
In this thesis, we address the issue of semantic gap with a structured learning
framework to allow modular extraction of visual semantics. Semantic image regions
(e.g. face, building, sky etc) are learned statistically, detected directly from image
without segmentation, reconciled across multiple scales, and aggregated spatially to
form compact semantic index. To circumvent the ambiguity and subjectivity in a
query, a new query method that allows spatial arrangement of visual semantics is
proposed. A query is represented as a disjunctive normal form of visual query terms
and processed using fuzzy set operators.
A drawback of supervised learning is the manual labeling of regions as training
samples. In this thesis, a new learning framework to discover local semantic patterns
and to generate their samples for training with minimal human intervention has been
developed. The discovered patterns can be visualized and used in semantic indexing.
In addition, three new class-based indexing schemes are explored. The winnertake-
all scheme supports class-based image retrieval. The class relative scheme and
the local classification scheme compute inter-class memberships and local class patterns
as indexes for similarity matching respectively. A Bayesian formulation is
proposed to unify local and global indexes in image comparison and ranking that
resulted in superior image retrieval performance over those of single indexes.
Query-by-example experiments on 2400 consumer photos with 16 semantic queries
show that the proposed approaches have significantly better (18% to 55%) average
precisions than a high-dimension feature fusion approach. The thesis has paved
two promising research directions, namely the semantics design approach and the
semantics discovery approach. They form elegant dual frameworks that exploits
pattern classifiers in learning and integrating local and global image semantics
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