22,860 research outputs found
PAC-Bayesian Majority Vote for Late Classifier Fusion
A lot of attention has been devoted to multimedia indexing over the past few
years. In the literature, we often consider two kinds of fusion schemes: The
early fusion and the late fusion. In this paper we focus on late classifier
fusion, where one combines the scores of each modality at the decision level.
To tackle this problem, we investigate a recent and elegant well-founded
quadratic program named MinCq coming from the Machine Learning PAC-Bayes
theory. MinCq looks for the weighted combination, over a set of real-valued
functions seen as voters, leading to the lowest misclassification rate, while
making use of the voters' diversity. We provide evidence that this method is
naturally adapted to late fusion procedure. We propose an extension of MinCq by
adding an order- preserving pairwise loss for ranking, helping to improve Mean
Averaged Precision measure. We confirm the good behavior of the MinCq-based
fusion approaches with experiments on a real image benchmark.Comment: 7 pages, Research repor
Deep Multi-Modal Classification of Intraductal Papillary Mucinous Neoplasms (IPMN) with Canonical Correlation Analysis
Pancreatic cancer has the poorest prognosis among all cancer types.
Intraductal Papillary Mucinous Neoplasms (IPMNs) are radiographically
identifiable precursors to pancreatic cancer; hence, early detection and
precise risk assessment of IPMN are vital. In this work, we propose a
Convolutional Neural Network (CNN) based computer aided diagnosis (CAD) system
to perform IPMN diagnosis and risk assessment by utilizing multi-modal MRI. In
our proposed approach, we use minimum and maximum intensity projections to ease
the annotation variations among different slices and type of MRIs. Then, we
present a CNN to obtain deep feature representation corresponding to each MRI
modality (T1-weighted and T2-weighted). At the final step, we employ canonical
correlation analysis (CCA) to perform a fusion operation at the feature level,
leading to discriminative canonical correlation features. Extracted features
are used for classification. Our results indicate significant improvements over
other potential approaches to solve this important problem. The proposed
approach doesn't require explicit sample balancing in cases of imbalance
between positive and negative examples. To the best of our knowledge, our study
is the first to automatically diagnose IPMN using multi-modal MRI.Comment: Accepted for publication in IEEE International Symposium on
Biomedical Imaging (ISBI) 201
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