2,469 research outputs found
Deep Adaptive Attention for Joint Facial Action Unit Detection and Face Alignment
Facial action unit (AU) detection and face alignment are two highly
correlated tasks since facial landmarks can provide precise AU locations to
facilitate the extraction of meaningful local features for AU detection. Most
existing AU detection works often treat face alignment as a preprocessing and
handle the two tasks independently. In this paper, we propose a novel
end-to-end deep learning framework for joint AU detection and face alignment,
which has not been explored before. In particular, multi-scale shared features
are learned firstly, and high-level features of face alignment are fed into AU
detection. Moreover, to extract precise local features, we propose an adaptive
attention learning module to refine the attention map of each AU adaptively.
Finally, the assembled local features are integrated with face alignment
features and global features for AU detection. Experiments on BP4D and DISFA
benchmarks demonstrate that our framework significantly outperforms the
state-of-the-art methods for AU detection.Comment: This paper has been accepted by ECCV 201
Ensemble of Hankel Matrices for Face Emotion Recognition
In this paper, a face emotion is considered as the result of the composition
of multiple concurrent signals, each corresponding to the movements of a
specific facial muscle. These concurrent signals are represented by means of a
set of multi-scale appearance features that might be correlated with one or
more concurrent signals. The extraction of these appearance features from a
sequence of face images yields to a set of time series. This paper proposes to
use the dynamics regulating each appearance feature time series to recognize
among different face emotions. To this purpose, an ensemble of Hankel matrices
corresponding to the extracted time series is used for emotion classification
within a framework that combines nearest neighbor and a majority vote schema.
Experimental results on a public available dataset shows that the adopted
representation is promising and yields state-of-the-art accuracy in emotion
classification.Comment: Paper to appear in Proc. of ICIAP 2015. arXiv admin note: text
overlap with arXiv:1506.0500
An Elastic Interaction-Based Loss Function for Medical Image Segmentation
Deep learning techniques have shown their success in medical image
segmentation since they are easy to manipulate and robust to various types of
datasets. The commonly used loss functions in the deep segmentation task are
pixel-wise loss functions. This results in a bottleneck for these models to
achieve high precision for complicated structures in biomedical images. For
example, the predicted small blood vessels in retinal images are often
disconnected or even missed under the supervision of the pixel-wise losses.
This paper addresses this problem by introducing a long-range elastic
interaction-based training strategy. In this strategy, convolutional neural
network (CNN) learns the target region under the guidance of the elastic
interaction energy between the boundary of the predicted region and that of the
actual object. Under the supervision of the proposed loss, the boundary of the
predicted region is attracted strongly by the object boundary and tends to stay
connected. Experimental results show that our method is able to achieve
considerable improvements compared to commonly used pixel-wise loss functions
(cross entropy and dice Loss) and other recent loss functions on three retinal
vessel segmentation datasets, DRIVE, STARE and CHASEDB1
A MIQE-Compliant Real-Time PCR Assay for Aspergillus Detection
PMCID: PMC3393739This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Selection for Replicases in Protocells
PMCID: PMC3649988This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Detectability of colorectal neoplasia with fluorine-18-2-fluoro-2-deoxy-D-glucose positron emission tomography and computed tomography (FDG-PET/CT)
The purpose of this study was to analyze the detectability of colorectal neoplasia with fluorine-18-2-fluoro-2-deoxy-d-glucose positron emission tomography/computed tomography (FDG-PET/CT).
Data for a total of 492 patients who had undergone both PET/CT and colonoscopy were analyzed. After the findings of PET/CT and colonoscopy were determined independently, the results were compared in each of the six colonic sites examined in all patients. The efficacy of PET/CT was determined using colonoscopic examination as the gold standard.
In all, 270 colorectal lesions 5 mm or more in size, including 70 pathologically confirmed malignant lesions, were found in 172 patients by colonoscopy. The sensitivity and specificity of PET/CT for detecting any of the colorectal lesions were 36 and 98%, respectively. For detecting lesions 11 mm or larger, the sensitivity was increased to 85%, with the specificity remaining consistent (97%). Moreover, the sensitivity for tumors 21 mm or larger was 96% (48/50). Tumors with malignant or high-grade pathology were likely to be positive with PET/CT. A size of 10 mm or smaller [odds ratio (OR) 44.14, 95% confidence interval (95% CI) 11.44-221.67] and flat morphology (OR 7.78, 95% CI 1.79-36.25) were significant factors that were associated with false-negative cases on PET/CT.
The sensitivity of PET/CT for detecting colorectal lesions is acceptable, showing size- and pathology-dependence, suggesting, for the most part, that clinically relevant lesions are detectable with PET/CT. However, when considering PET/CT for screening purposes caution must be exercised because there are cases of false-negative results
Murine model for Fusarium oxysporum invasive fusariosis reveals organ-specific structures for dissemination and long-term persistence
Peer reviewedPublisher PD
Selective area epitaxy of ultra-high density InGaN quantum dots by diblock copolymer lithography
Highly uniform InGaN-based quantum dots (QDs) grown on a nanopatterned dielectric layer defined by self-assembled diblock copolymer were performed by metal-organic chemical vapor deposition. The cylindrical-shaped nanopatterns were created on SiNx layers deposited on a GaN template, which provided the nanopatterning for the epitaxy of ultra-high density QD with uniform size and distribution. Scanning electron microscopy and atomic force microscopy measurements were conducted to investigate the QDs morphology. The InGaN/GaN QDs with density up to 8 × 1010 cm-2 are realized, which represents ultra-high dot density for highly uniform and well-controlled, nitride-based QDs, with QD diameter of approximately 22-25 nm. The photoluminescence (PL) studies indicated the importance of NH3 annealing and GaN spacer layer growth for improving the PL intensity of the SiNx-treated GaN surface, to achieve high optical-quality QDs applicable for photonics devices
MBA: a literature mining system for extracting biomedical abbreviations
<p>Abstract</p> <p>Background</p> <p>The exploding growth of the biomedical literature presents many challenges for biological researchers. One such challenge is from the use of a great deal of abbreviations. Extracting abbreviations and their definitions accurately is very helpful to biologists and also facilitates biomedical text analysis. Existing approaches fall into four broad categories: rule based, machine learning based, text alignment based and statistically based. State of the art methods either focus exclusively on acronym-type abbreviations, or could not recognize rare abbreviations. We propose a systematic method to extract abbreviations effectively. At first a scoring method is used to classify the abbreviations into acronym-type and non-acronym-type abbreviations, and then their corresponding definitions are identified by two different methods: text alignment algorithm for the former, statistical method for the latter.</p> <p>Results</p> <p>A literature mining system MBA was constructed to extract both acronym-type and non-acronym-type abbreviations. An abbreviation-tagged literature corpus, called Medstract gold standard corpus, was used to evaluate the system. MBA achieved a recall of 88% at the precision of 91% on the Medstract gold-standard EVALUATION Corpus.</p> <p>Conclusion</p> <p>We present a new literature mining system MBA for extracting biomedical abbreviations. Our evaluation demonstrates that the MBA system performs better than the others. It can identify the definition of not only acronym-type abbreviations including a little irregular acronym-type abbreviations (e.g., <CNS1, cyclophilin seven suppressor>), but also non-acronym-type abbreviations (e.g., <Fas, CD95>).</p
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