332 research outputs found
Challenging Current Semi-Supervised Anomaly Segmentation Methods for Brain MRI
In this work, we tackle the problem of Semi-Supervised Anomaly Segmentation
(SAS) in Magnetic Resonance Images (MRI) of the brain, which is the task of
automatically identifying pathologies in brain images. Our work challenges the
effectiveness of current Machine Learning (ML) approaches in this application
domain by showing that thresholding Fluid-attenuated inversion recovery (FLAIR)
MR scans provides better anomaly segmentation maps than several different
ML-based anomaly detection models. Specifically, our method achieves better
Dice similarity coefficients and Precision-Recall curves than the competitors
on various popular evaluation data sets for the segmentation of tumors and
multiple sclerosis lesions.Comment: 10 pages, 4 figures, accepted to the MICCAI 2021 BrainLes Worksho
A Critical Analysis of the Carolina Leadership Academy’s CREED Program at the University of North Carolina-Chapel Hill
Student-athletes often know how to recognize leadership, but struggle to master techniques to best exemplify leadership characteristics. In 2003, sport psychologist Jeff Janssen partnered with the University of North Carolina at Chapel Hill (UNC) Athletics Department to create a learning environment fostering leadership within the unique special population of student-athletes. The creation of the Carolina Leadership Academy (CLA), comprised of a three-tiered formal leadership development curriculum, set UNC apart as a leader in higher education and intercollegiate athletics leadership development programs by helping student-athletes, administrators and coaches understand and foster leadership best practices. This formal leadership curriculum begins with the Carolina CREED program completed by all first year student-athletes at UNC. The intent of this mandatory foundational program is to introduce freshmen to the importance of personal leadership development. Quantitative and qualitative analysis were used to analyze research questions and examine the relationship between gender and student-athletes' perceived effectiveness of the CREED program curriculum components. A survey including twelve five-point Likert-scale and four open-ended questions was electronically transmitted to 211 student-athletes who completed the CREED program in the Spring 2008 semester. Other large universities look to UNC as a model for future leadership programming options. Therefore, these research findings from the Carolina CREED program curriculum will enable other intercollegiate athletic departments nationwide to improve student-athlete first-year leadership development programming
Unsupervised Pathology Detection: A Deep Dive Into the State of the Art
Deep unsupervised approaches are gathering increased attention for
applications such as pathology detection and segmentation in medical images
since they promise to alleviate the need for large labeled datasets and are
more generalizable than their supervised counterparts in detecting any kind of
rare pathology. As the Unsupervised Anomaly Detection (UAD) literature
continuously grows and new paradigms emerge, it is vital to continuously
evaluate and benchmark new methods in a common framework, in order to reassess
the state-of-the-art (SOTA) and identify promising research directions. To this
end, we evaluate a diverse selection of cutting-edge UAD methods on multiple
medical datasets, comparing them against the established SOTA in UAD for brain
MRI. Our experiments demonstrate that newly developed feature-modeling methods
from the industrial and medical literature achieve increased performance
compared to previous work and set the new SOTA in a variety of modalities and
datasets. Additionally, we show that such methods are capable of benefiting
from recently developed self-supervised pre-training algorithms, further
increasing their performance. Finally, we perform a series of experiments in
order to gain further insights into some unique characteristics of selected
models and datasets. Our code can be found under
https://github.com/iolag/UPD_study/.Comment: 12 pages, 4 figures, accepted for publication in IEEE Transactions on
Medical Imaging (added copyright, DOI information
Unsupervised Anomaly Localization with Structural Feature-Autoencoders
Unsupervised Anomaly Detection has become a popular method to detect
pathologies in medical images as it does not require supervision or labels for
training. Most commonly, the anomaly detection model generates a "normal"
version of an input image, and the pixel-wise -difference of the two is
used to localize anomalies. However, large residuals often occur due to
imperfect reconstruction of the complex anatomical structures present in most
medical images. This method also fails to detect anomalies that are not
characterized by large intensity differences to the surrounding tissue. We
propose to tackle this problem using a feature-mapping function that transforms
the input intensity images into a space with multiple channels where anomalies
can be detected along different discriminative feature maps extracted from the
original image. We then train an Autoencoder model in this space using
structural similarity loss that does not only consider differences in intensity
but also in contrast and structure. Our method significantly increases
performance on two medical data sets for brain MRI. Code and experiments are
available at https://github.com/FeliMe/feature-autoencoderComment: 10 pages, 5 figures, one table, accepted to the MICCAI 2021 BrainLes
Worksho
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