2,469 research outputs found
Recurrent Saliency Transformation Network: Incorporating Multi-Stage Visual Cues for Small Organ Segmentation
We aim at segmenting small organs (e.g., the pancreas) from abdominal CT
scans. As the target often occupies a relatively small region in the input
image, deep neural networks can be easily confused by the complex and variable
background. To alleviate this, researchers proposed a coarse-to-fine approach,
which used prediction from the first (coarse) stage to indicate a smaller input
region for the second (fine) stage. Despite its effectiveness, this algorithm
dealt with two stages individually, which lacked optimizing a global energy
function, and limited its ability to incorporate multi-stage visual cues.
Missing contextual information led to unsatisfying convergence in iterations,
and that the fine stage sometimes produced even lower segmentation accuracy
than the coarse stage.
This paper presents a Recurrent Saliency Transformation Network. The key
innovation is a saliency transformation module, which repeatedly converts the
segmentation probability map from the previous iteration as spatial weights and
applies these weights to the current iteration. This brings us two-fold
benefits. In training, it allows joint optimization over the deep networks
dealing with different input scales. In testing, it propagates multi-stage
visual information throughout iterations to improve segmentation accuracy.
Experiments in the NIH pancreas segmentation dataset demonstrate the
state-of-the-art accuracy, which outperforms the previous best by an average of
over 2%. Much higher accuracies are also reported on several small organs in a
larger dataset collected by ourselves. In addition, our approach enjoys better
convergence properties, making it more efficient and reliable in practice.Comment: Accepted to CVPR 2018 (10 pages, 6 figures
One-Sided Error Probabalistic Inductive Interface and Reliable Frequency Identification
For EX- and BC-type identification, one-sided error probabilistic inference and reliable frequency identification on sets of functions are introduced. In particular, we relate the one to the other and characterize one-sided error probabilistic inference to exactly coincide with reliable frequency identification, on any setM. Moreover, we show that reliable EX and BC-frequency inference forms a new discrete hierarchy having the breakpoints 1, l/2, l/3, ..
One-Sided Error Probabalistic Inductive Interface and Reliable Frequency Identification
For EX- and BC-type identification, one-sided error probabilistic inference and reliable frequency identification on sets of functions are introduced. In particular, we relate the one to the other and characterize one-sided error probabilistic inference to exactly coincide with reliable frequency identification, on any setM. Moreover, we show that reliable EX and BC-frequency inference forms a new discrete hierarchy having the breakpoints 1, l/2, l/3, ..
Tractability Frontiers in Probabilistic Team Semantics and Existential Second-Order Logic over the Reals
Peer reviewe
Information Extraction on Para-Relational Data.
Para-relational data (such as spreadsheets and diagrams) refers to a type of nearly
relational data that shares the important qualities of relational data but does not
present itself in a relational format. Para-relational data often conveys highly valuable
information and is widely used in many different areas. If we can convert para-relational
data into the relational format, many existing tools can be leveraged for a
variety of interesting applications, such as data analysis with relational query systems
and data integration applications.
This dissertation aims to convert para-relational data into a high-quality relational
form with little user assistance. We have developed four standalone systems, each
addressing a specific type of para-relational data. Senbazuru is a prototype spreadsheet
database management system that extracts relational information from a large
number of spreadsheets. Anthias is an extension of the Senbazuru system to convert
a broader range of spreadsheets into a relational format. Lyretail is an extraction
system to detect long-tail dictionary entities on webpages. Finally, DiagramFlyer is
a web-based search system that obtains a large number of diagrams automatically
extracted from web-crawled PDFs. Together, these four systems demonstrate that
converting para-relational data into the relational format is possible today, and also
suggest directions for future systems.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120853/1/chenzhe_1.pd
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