7,643 research outputs found
An unusual timing for symptomatic chest pain in an adult chest wall myofibroma: a case report
INTRODUCTION: Myofibromas are benign mesenchymal neoplasms that can present as solitary and multicentric lesions. They can occur in several locations and can occur at any age from neonates to elderly patients. However, most of the lesions are found in neonates and babies. It rarely occurs in adults. CASE PRESENTATION: A 29-year-old Taiwanese man presented with persistent dull chest pain in his right lateral chest wall for 2 weeks. A chest X-ray showed a faint patchy opacity over the periphery of his right upper lung zone. Computed tomography and magnetic resonance imaging showed a lobulated mass at the intercostal space between his right fifth and sixth ribs with contrast enhancement and bone invasion. Malignancy could not be excluded. A percutaneous needle aspiration biopsy failed due to technique issues, so he underwent a thoracotomy and the tumor was excised with Marlex mesh repairs for the thoracic defect. Pathology confirmed a myofibroma without malignancy. He recovered uneventfully and no local recurrence was detected at the 1-year follow-up examination. CONCLUSIONS: Chest wall myofibroma presenting with chest pain has never been reported in adults. It is a challenge to differentiate myofibroma from malignancy in chest wall preoperatively, such as seen in our patient. To the best of our knowledge, this has not been previously reported in the scientific literature. Although myofibroma rarely occurs in the chest wall and adults, it must be suspected in any chest wall tumor presenting with chest pain
Spectral Analysis for Semantic Segmentation with Applications on Feature Truncation and Weak Annotation
We propose spectral analysis to investigate the correlation between the
accuracy and the resolution of segmentation maps for semantic segmentation. The
current networks predict segmentation maps on the down-sampled grid of images
to alleviate the computational cost. Moreover, these networks can be trained by
weak annotations that utilize only the coarse contour of segmentation maps.
Despite the successful achievement of these works utilizing the low-frequency
information of segmentation maps, however, the accuracy of resultant
segmentation maps may also be degraded in the regions near object boundaries.
It is yet unclear for a theoretical guideline to determine an optimal
down-sampled grid to strike the balance between the cost and the accuracy of
segmentation. We analyze the objective function (cross-entropy) and network
back-propagation process in frequency domain. We discover that cross-entropy
and key features of CNN are mainly contributed by the low-frequency components
of segmentation maps. This further provides us quantitative results to
determine the efficacy of down-sampled grid of segmentation maps. The analysis
is then validated on the two applications: the feature truncation method and
the block-wise annotation that limit the high-frequency components of the CNN
features and annotation, respectively. The results agree with our analysis.
Thus the success of the existing work utilizing low-frequency information of
segmentation maps now has theoretical foundation.Comment: 21 page
Learning satisfaction of undergraduates in single-sex-dominated academic fields in Taiwan
AbstractThe present study investigated relationships between undergraduates’ learning satisfaction, academic identity, self-esteem and feeling of depression and loneliness in Taiwan. Data were from a national survey in Taiwan. Participants were 15,706 third-year undergraduates (8719 female, 6987 male). The results showed that, after controlling for undergraduates’ academic performance and attitudes toward university and department, (1) learning satisfaction of females in male-dominant fields was negatively correlated with their feeling of depression, (2) learning satisfaction of males in female-dominant fields was positively correlated with their academic identity and self-esteem, and (3) learning satisfaction of undergraduates in non-dominated fields was positively correlated with their academic identity and self-esteem but also negatively correlated with their feelings of depression
BiRA-Net: Bilinear Attention Net for Diabetic Retinopathy Grading
Diabetic retinopathy (DR) is a common retinal disease that leads to
blindness. For diagnosis purposes, DR image grading aims to provide automatic
DR grade classification, which is not addressed in conventional research
methods of binary DR image classification. Small objects in the eye images,
like lesions and microaneurysms, are essential to DR grading in medical
imaging, but they could easily be influenced by other objects. To address these
challenges, we propose a new deep learning architecture, called BiRA-Net, which
combines the attention model for feature extraction and bilinear model for
fine-grained classification. Furthermore, in considering the distance between
different grades of different DR categories, we propose a new loss function,
called grading loss, which leads to improved training convergence of the
proposed approach. Experimental results are provided to demonstrate the
superior performance of the proposed approach.Comment: Accepted at ICIP 201
What can the structure of the palmprint tell us?
Since the olden days, palmistry has been used to foretell a person?s character traits and fate. The study of various lines and mounts of the palm helps the reader predict the person?s future. As research on palmprints progresses, there had been more uses of it being discovered, such as health prediction. These researches surface the need to further identify and make use of the mysteries of the palm. This paper aims to understand the different researches and its methods in disseminating the information for fortune telling and health prediction palmistry. With these understandings, the possibility of creating a database and query system for fortune telling and health prediction information is explored
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