3,321 research outputs found
Improving High Resolution Histology Image Classification with Deep Spatial Fusion Network
Histology imaging is an essential diagnosis method to finalize the grade and
stage of cancer of different tissues, especially for breast cancer diagnosis.
Specialists often disagree on the final diagnosis on biopsy tissue due to the
complex morphological variety. Although convolutional neural networks (CNN)
have advantages in extracting discriminative features in image classification,
directly training a CNN on high resolution histology images is computationally
infeasible currently. Besides, inconsistent discriminative features often
distribute over the whole histology image, which incurs challenges in
patch-based CNN classification method. In this paper, we propose a novel
architecture for automatic classification of high resolution histology images.
First, an adapted residual network is employed to explore hierarchical features
without attenuation. Second, we develop a robust deep fusion network to utilize
the spatial relationship between patches and learn to correct the prediction
bias generated from inconsistent discriminative feature distribution. The
proposed method is evaluated using 10-fold cross-validation on 400 high
resolution breast histology images with balanced labels and reports 95%
accuracy on 4-class classification and 98.5% accuracy, 99.6% AUC on 2-class
classification (carcinoma and non-carcinoma), which substantially outperforms
previous methods and close to pathologist performance.Comment: 8 pages, MICCAI workshop preceeding
Particle Gibbs for Bayesian Additive Regression Trees
Additive regression trees are flexible non-parametric models and popular
off-the-shelf tools for real-world non-linear regression. In application
domains, such as bioinformatics, where there is also demand for probabilistic
predictions with measures of uncertainty, the Bayesian additive regression
trees (BART) model, introduced by Chipman et al. (2010), is increasingly
popular. As data sets have grown in size, however, the standard
Metropolis-Hastings algorithms used to perform inference in BART are proving
inadequate. In particular, these Markov chains make local changes to the trees
and suffer from slow mixing when the data are high-dimensional or the best
fitting trees are more than a few layers deep. We present a novel sampler for
BART based on the Particle Gibbs (PG) algorithm (Andrieu et al., 2010) and a
top-down particle filtering algorithm for Bayesian decision trees
(Lakshminarayanan et al., 2013). Rather than making local changes to individual
trees, the PG sampler proposes a complete tree to fit the residual. Experiments
show that the PG sampler outperforms existing samplers in many settings
Efficient & Effective Selective Query Rewriting with Efficiency Predictions
To enhance effectiveness, a user's query can be rewritten internally by the search engine in many ways, for example by applying proximity, or by expanding the query with related terms. However, approaches that benefit effectiveness often have a negative impact on efficiency, which has impacts upon the user satisfaction, if the query is excessively slow. In this paper, we propose a novel framework for using the predicted execution time of various query rewritings to select between alternatives on a per-query basis, in a manner that ensures both effectiveness and efficiency. In particular, we propose the prediction of the execution time of ephemeral (e.g., proximity) posting lists generated from uni-gram inverted index posting lists, which are used in establishing the permissible query rewriting alternatives that may execute in the allowed time. Experiments examining both the effectiveness and efficiency of the proposed approach demonstrate that a 49% decrease in mean response time (and 62% decrease in 95th-percentile response time) can be attained without significantly hindering the effectiveness of the search engine
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