Bone density and depression in premenopausal South African women: a pilot study

Objective: It is posited that the effect of depression on BMD is dependent on the severity of depression. Conflicting evidence exists regarding this possible association. This study investigated the association between depression and low bone mineral density (BMD). Methods: The hypothesis was investigated in a random sample of volunteers (n=40) and in premenopausal female psychiatric patients (n=5) diagnosed with recurrent severe major depression. The outcome measures were BMD (DEXA); depression (Beck Depression Inventory and Psychological General Well-being Scale) and 24-hour saliva cortisol levels (ELISA). In a comparison of women (4 of the 40 i.e. “control” subjects) with negligible symptoms of depression and the five patients with severe recurrent major depression- BMD, depression, saliva cortisol and bone turnover markers were measured and compared. Pro-inflammatory status (IL-1 and TNF-alpha) was investigated in the psychiatric patients only. Results: In the random – non clinical - sample of women (n=40), 26 exhibited normal BMD and 14 exhibited low BMD. Depressive symptoms and cortisol levelswere not significantly different between these two groups. Women with severe recurrent major depression(n=5) exhibited lower median BMD T-scores, higher overall bone turnover and higher 24-hour cortisol levels compared to “control” subjects (n=4). The psychiatric patients also exhibited elevated IL-1 levels. Conclusion: The effect of depression on BMD may be dependent on the depression severity. IL-1 and cortisol are possible mediators in depression-induced BMD loss.Key words: Bone mineral density; Cortisol; Depression; Pro-inflammatory cytokine

Nationally hosted tax intervention: Exploring South African students' perceptions of its usefulness to develop pervasive skills

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Out-of-Distribution Detection of Melanoma using Normalizing Flows

Generative modelling has been a topic at the forefront of machine learning research for a substantial amount of time. With the recent success in the field of machine learning, especially in deep learning, there has been an increased interest in explainable and interpretable machine learning. The ability to model distributions and provide insight in the density estimation and exact data likelihood is an example of such a feature. Normalizing Flows (NFs), a relatively new research field of generative modelling, has received substantial attention since it is able to do exactly this at a relatively low cost whilst enabling competitive generative results. While the generative abilities of NFs are typically explored, we focus on exploring the data distribution modelling for Out-of-Distribution (OOD) detection. Using one of the state-of-the-art NF models, GLOW, we attempt to detect OOD examples in the ISIC dataset. We notice that this model under performs in conform related research. To improve the OOD detection, we explore the masking methods to inhibit co-adaptation of the coupling layers however find no substantial improvement. Furthermore, we utilize Wavelet Flow which uses wavelets that can filter particular frequency components, thus simplifying the modeling process to data-driven conditional wavelet coefficients instead of complete images. This enables us to efficiently model larger resolution images in the hopes that it would capture more relevant features for OOD. The paper that introduced Wavelet Flow mainly focuses on its ability of sampling high resolution images and did not treat OOD detection. We present the results and propose several ideas for improvement such as controlling frequency components, using different wavelets and using other state-of-the-art NF architectures

Towards real-time 6D pose estimation of objects in single-view cone-beam X-ray

Deep learning-based pose estimation algorithms can successfully estimate the pose of objects in an image, especially in the field of color images. 6D Object pose estimation based on deep learning models for X-ray images often use custom architectures that employ extensive CAD models and simulated data for training purposes. Recent RGB-based methods opt to solve pose estimation problems using small datasets, making them more attractive for the X-ray domain where medical data is scarcely available. We refine an existing RGB-based model (SingleShotPose) to estimate the 6D pose of a marked cube from grayscale X-ray images by creating a generic solution trained on only real X-ray data and adjusted for X-ray acquisition geometry. The model regresses 2D control points and calculates the pose through 2D/3D correspondences using Perspective-n-Point(PnP), allowing a single trained model to be used across all supporting cone-beam-based X-ray geometries. Since modern X-ray systems continuously adjust acquisition parameters during a procedure, it is essential for such a pose estimation network to consider these parameters in order to be deployed successfully and find a real use case. With a 5-cm/5-degree accuracy of 93% and an average 3D rotation error of 2.2 degrees, the results of the proposed approach are comparable with state-of-the-art alternatives, while requiring significantly less real training examples and being applicable in real-time applications.Comment: Published at SPIE Medical Imaging 202

Investigating and Improving Latent Density Segmentation Models for Aleatoric Uncertainty Quantification in Medical Imaging

Data uncertainties, such as sensor noise or occlusions, can introduce irreducible ambiguities in images, which result in varying, yet plausible, semantic hypotheses. In Machine Learning, this ambiguity is commonly referred to as aleatoric uncertainty. Latent density models can be utilized to address this problem in image segmentation. The most popular approach is the Probabilistic U-Net (PU-Net), which uses latent Normal densities to optimize the conditional data log-likelihood Evidence Lower Bound. In this work, we demonstrate that the PU- Net latent space is severely inhomogenous. As a result, the effectiveness of gradient descent is inhibited and the model becomes extremely sensitive to the localization of the latent space samples, resulting in defective predictions. To address this, we present the Sinkhorn PU-Net (SPU-Net), which uses the Sinkhorn Divergence to promote homogeneity across all latent dimensions, effectively improving gradient-descent updates and model robustness. Our results show that by applying this on public datasets of various clinical segmentation problems, the SPU-Net receives up to 11% performance gains compared against preceding latent variable models for probabilistic segmentation on the Hungarian-Matched metric. The results indicate that by encouraging a homogeneous latent space, one can significantly improve latent density modeling for medical image segmentation.Comment: 12 pages incl. references, 11 figure

Estas son algunas de las habilidades blandas demandadas en Colombia

Este producto forma parte de una serie de infografías de divulgación científica que buscan reseñar algunas de las investigaciones más importantes en las que ha tenido participación la Universidad EAFIT, publicadas en las revistas especializadas más prestigiosas del mund