Deformable Registration through Learning of Context-Specific Metric Aggregation

We propose a novel weakly supervised discriminative algorithm for learning context specific registration metrics as a linear combination of conventional similarity measures. Conventional metrics have been extensively used over the past two decades and therefore both their strengths and limitations are known. The challenge is to find the optimal relative weighting (or parameters) of different metrics forming the similarity measure of the registration algorithm. Hand-tuning these parameters would result in sub optimal solutions and quickly become infeasible as the number of metrics increases. Furthermore, such hand-crafted combination can only happen at global scale (entire volume) and therefore will not be able to account for the different tissue properties. We propose a learning algorithm for estimating these parameters locally, conditioned to the data semantic classes. The objective function of our formulation is a special case of non-convex function, difference of convex function, which we optimize using the concave convex procedure. As a proof of concept, we show the impact of our approach on three challenging datasets for different anatomical structures and modalities.Comment: Accepted for publication in the 8th International Workshop on Machine Learning in Medical Imaging (MLMI 2017), in conjunction with MICCAI 201

Controlling Fairness and Bias in Dynamic Learning-to-Rank

Rankings are the primary interface through which many online platforms match users to items (e.g. news, products, music, video). In these two-sided markets, not only the users draw utility from the rankings, but the rankings also determine the utility (e.g. exposure, revenue) for the item providers (e.g. publishers, sellers, artists, studios). It has already been noted that myopically optimizing utility to the users, as done by virtually all learning-to-rank algorithms, can be unfair to the item providers. We, therefore, present a learning-to-rank approach for explicitly enforcing merit-based fairness guarantees to groups of items (e.g. articles by the same publisher, tracks by the same artist). In particular, we propose a learning algorithm that ensures notions of amortized group fairness, while simultaneously learning the ranking function from implicit feedback data. The algorithm takes the form of a controller that integrates unbiased estimators for both fairness and utility, dynamically adapting both as more data becomes available. In addition to its rigorous theoretical foundation and convergence guarantees, we find empirically that the algorithm is highly practical and robust.Comment: First two authors contributed equally. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval 202

Implications of neglect and caregiving during childhood for maternal pregnancy spacing

Rapid repeat pregnancies (RRP) are associated with higher risk of adverse outcomes for maternal and child health. Previous research has identified numerous risk factors for RRP, but none have studied the link between maternal adverse childhood experiences (ACEs) and RRP. Our study examines this association, as well as the potential factors that moderate the impact of ACEs in RRP. In a clinic-based sample of 111 women with high levels of childhood adversity on average, those who experienced childhood neglect had more RRP than those who did not. However, this was not true for women who experienced neglect and acted as caregivers during childhood. Further research is needed to explore this interaction and its implications for (1) treatment of childhood neglect and (2) prevention of RRP

Learning to rank from medical imaging data

Medical images can be used to predict a clinical score coding for the severity of a disease, a pain level or the complexity of a cognitive task. In all these cases, the predicted variable has a natural order. While a standard classifier discards this information, we would like to take it into account in order to improve prediction performance. A standard linear regression does model such information, however the linearity assumption is likely not be satisfied when predicting from pixel intensities in an image. In this paper we address these modeling challenges with a supervised learning procedure where the model aims to order or rank images. We use a linear model for its robustness in high dimension and its possible interpretation. We show on simulations and two fMRI datasets that this approach is able to predict the correct ordering on pairs of images, yielding higher prediction accuracy than standard regression and multiclass classification techniques

Evolving rules for document classification

We describe a novel method for using Genetic Programming to create compact classification rules based on combinations of N-Grams (character strings). Genetic programs acquire fitness by producing rules that are effective classifiers in terms of precision and recall when evaluated against a set of training documents. We describe a set of functions and terminals and provide results from a classification task using the Reuters 21578 dataset. We also suggest that because the induced rules are meaningful to a human analyst they may have a number of other uses beyond classification and provide a basis for text mining applications

Maternal Adverse and Protective Childhood Experiences and Prenatal Smoking

Prenatal smoking is associated with adverse pregnancy and birth outcomes as well as health problems in early childhood. Recent research determined that maternal adverse childhood experiences (ACEs) increase the odds of smoking during pregnancy. We consider the role of protective and compensatory childhood experiences (PACEs) in an effort to examine the extent to which positive childhood experiences are protective factors for maternal smoking behaviors. Between 2015-2018, 309 pregnant women in Oklahoma recruited from high-risk prenatal clinics, childbirth education classes, and social media were surveyed about their childhood experiences and smoking behaviors during pregnancy. Ordinal regression analysis was used to examine the association between ACEs, PACEs, and prenatal smoking frequency. Similar to prior studies, we found women with more ACEs reported smoking more frequently during pregnancy. Women with more PACEs reported significantly less frequent prenatal smoking. With both ACEs and PACEs in the model, however, ACEs was no longer a significant predictor of maternal prenatal smoking. Our findings suggest that protective and compensatory childhood experiences may be more salient for prenatal smoking behaviors than adverse childhood experiences. Identifying protective factors for pregnancy health risk behaviors such as smoking can offer a target for intervention and prevention

Legal Judgement Prediction for UK Courts

Legal Judgement Prediction (LJP) is the task of automatically predicting the outcome of a court case given only the case document. During the last five years researchers have successfully attempted this task for the supreme courts of three jurisdictions: the European Union, France, and China. Motivation includes the many real world applications including: a prediction system that can be used at the judgement drafting stage, and the identification of the most important words and phrases within a judgement. The aim of our research was to build, for the first time, an LJP model for UK court cases. This required the creation of a labelled data set of UK court judgements and the subsequent application of machine learning models. We evaluated different feature representations and different algorithms. Our best performing model achieved: 69.05% accuracy and 69.02 F1 score. We demonstrate that LJP is a promising area of further research for UK courts by achieving high model performance and the ability to easily extract useful features

Hyperparameter Importance Across Datasets

With the advent of automated machine learning, automated hyperparameter optimization methods are by now routinely used in data mining. However, this progress is not yet matched by equal progress on automatic analyses that yield information beyond performance-optimizing hyperparameter settings. In this work, we aim to answer the following two questions: Given an algorithm, what are generally its most important hyperparameters, and what are typically good values for these? We present methodology and a framework to answer these questions based on meta-learning across many datasets. We apply this methodology using the experimental meta-data available on OpenML to determine the most important hyperparameters of support vector machines, random forests and Adaboost, and to infer priors for all their hyperparameters. The results, obtained fully automatically, provide a quantitative basis to focus efforts in both manual algorithm design and in automated hyperparameter optimization. The conducted experiments confirm that the hyperparameters selected by the proposed method are indeed the most important ones and that the obtained priors also lead to statistically significant improvements in hyperparameter optimization.Comment: \c{opyright} 2018. Copyright is held by the owner/author(s). Publication rights licensed to ACM. This is the author's version of the work. It is posted here for your personal use, not for redistribution. The definitive Version of Record was published in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Minin

Boric acid vaginal suppositories: a brief review.

OBJECTIVE: The purpose of this study was to determine the utility of serum CA125 determinations in diagnosing acute salpingitis. METHODS: CA125 levels were determined for 34 women with the clinical diagnosis of pelvic inflammatory disease (PID). Acute salpingitis was confirmed laparoscopically in 28 women (82.3%). RESULTS: Twenty patients (71.4%) with laparoscopically confirmed acute salpingitis had CA125 levels greater than 7.5 units, compared with no patients (0/6) with laparoscopically normal tubes (P = 0.002). The degree of elevation of CA125 levels correlated with the severity of tubal inflammation noted at laparoscopy. All patients with levels above 16 units had laparoscopically severe salpingitis. CONCLUSIONS: We conclude that while CA125 levels above 7.5 units may modestly improve the ability of the clinical diagnosis of PID to accurately reflect visually confirmed acute salpingitis, limitations of the test make its clinical utility questionable