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 $l^p$-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