Automatic aortic valve landmark localization in coronary CT angiography using colonial walk

The minimally invasive transcatheter aortic valve implantation (TAVI) is the most prevalent method to treat aortic valve stenosis. For pre-operative surgical planning, contrast-enhanced coronary CT angiography (CCTA) is used as the imaging technique to acquire 3-D measurements of the valve. Accurate localization of the eight aortic valve landmarks in CT images plays a vital role in the TAVI workflow because a small error risks blocking the coronary circulation. In order to examine the valve and mark the landmarks, physicians prefer a view parallel to the hinge plane, instead of using the conventional axial, coronal or sagittal view. However, customizing the view is a difficult and time-consuming task because of unclear aorta pose and different artifacts of CCTA. Therefore, automatic localization of landmarks can serve as a useful guide to the physicians customizing the viewpoint. In this paper, we present an automatic method to localize the aortic valve landmarks using colonial walk, a regression tree-based machine-learning algorithm. For efficient learning from the training set, we propose a two-phase optimized search space learning model in which a representative point inside the valvular area is first learned from the whole CT volume. All eight landmarks are then learned from a smaller area around that point. Experiment with preprocedural CCTA images of TAVI undergoing patients showed that our method is robust under high stenotic variation and notably efficient, as it requires only 12 milliseconds to localize all eight landmarks, as tested on a 3.60 GHz single-core CPU.ope

When Efficient Model Averaging Out-Performs Boosting and Bagging , To Appear ECML/PKDD

Abstract. Bayesian model averaging also known as the Bayes optimal classifier (BOC) is an ensemble technique used extensively in the statistics literature. However, compared to other ensemble techniques such as bagging and boosting, BOC is less known and rarely used in data mining. This is partly due to model averaging being perceived as being inefficient and because bagging and boosting consistently outperforms a single model, which raises the question: “Do we even need BOC in datamining?”. We show that the answer to this question is “yes ” by illustrating that several recent efficient model averaging approaches can significantly outperform bagging and boosting in realistic difficult situations such as extensive class label noise, sample selection bias and many-class problems. To our knowledge the insights that model averaging can outperform bagging and boosting in these situations has not been published in the machine learning, mining or statistical communities.

Deep Active Learning Explored Across Diverse Label Spaces

abstract: Deep learning architectures have been widely explored in computer vision and have depicted commendable performance in a variety of applications. A fundamental challenge in training deep networks is the requirement of large amounts of labeled training data. While gathering large quantities of unlabeled data is cheap and easy, annotating the data is an expensive process in terms of time, labor and human expertise. Thus, developing algorithms that minimize the human effort in training deep models is of immense practical importance. Active learning algorithms automatically identify salient and exemplar samples from large amounts of unlabeled data and can augment maximal information to supervised learning models, thereby reducing the human annotation effort in training machine learning models. The goal of this dissertation is to fuse ideas from deep learning and active learning and design novel deep active learning algorithms. The proposed learning methodologies explore diverse label spaces to solve different computer vision applications. Three major contributions have emerged from this work; (i) a deep active framework for multi-class image classication, (ii) a deep active model with and without label correlation for multi-label image classi- cation and (iii) a deep active paradigm for regression. Extensive empirical studies on a variety of multi-class, multi-label and regression vision datasets corroborate the potential of the proposed methods for real-world applications. Additional contributions include: (i) a multimodal emotion database consisting of recordings of facial expressions, body gestures, vocal expressions and physiological signals of actors enacting various emotions, (ii) four multimodal deep belief network models and (iii) an in-depth analysis of the effect of transfer of multimodal emotion features between source and target networks on classification accuracy and training time. These related contributions help comprehend the challenges involved in training deep learning models and motivate the main goal of this dissertation.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

The use of statistical models to estimate the timing and causes of neonatal deaths

Despite major reductions in child mortality, decrease in neonatal (first month of life) deaths has been substantially slower. To further reduce neonatal deaths, scale-up of relevant and timely health interventions is necessary. Such scale-up needs to be supported by evidence, but important gaps remain in our knowledge regarding the timing and causes of neonatal deaths. Birth and the days immediately following carry the highest daily risk of death, yet standard life tables do not present daily mortality risks within the neonatal period. Around three-quarters of neonatal deaths occur during the first week, and most interventions to prevent these deaths must be delivered very quickly. Thus, understanding the neonatal day-of-death distribution is important for delivering appropriate and timely interventions. We fitted an exponential function to survey data to model the daily neonatal mortality risk, focusing on the first day and week after birth. Using this model and observed data, we estimated the daily risk of death in the neonatal period for 186 countries in 2013. Targeted interventions also require reliable estimates of neonatal cause-of-death distributions. Cause-of-death estimation is challenging because of limited data quantity and quality in many countries. Previous work highlighted the need to expand the existing country-specific neonatal cause-of-death estimates and improve the methods. We developed a multinomial model to estimate the neonatal cause-of-death distribution by the early (days 0-6) and late (days 7-27) neonatal periods. We then focused on methodological improvements, including evaluating performance and developing a proof-of-concept Bayesian mixed effects model. This thesis straddles two topics that are receiving increased attention: cause-of-death estimation and neonatal health. Ideally, the results from this work can help current neonatal health policies and programmes while contributing to the growing area of cause-of-death modelling. However, the longer-term aim should be to improve data collection to obviate the need for statistical modelling exercises