Informative sample generation using class aware generative adversarial networks for classification of chest Xrays

Training robust deep learning (DL) systems for disease detection from medical images is challenging due to limited images covering different disease types and severity. The problem is especially acute, where there is a severe class imbalance. We propose an active learning (AL) framework to select most informative samples for training our model using a Bayesian neural network. Informative samples are then used within a novel class aware generative adversarial network (CAGAN) to generate realistic chest xray images for data augmentation by transferring characteristics from one class label to another. Experiments show our proposed AL framework is able to achieve state-of-the-art performance by using about $35\%$ of the full dataset, thus saving significant time and effort over conventional methods

Active Clinical Trials for Personalized Medicine

Individualized treatment rules (ITRs) tailor treatments according to individual patient characteristics. They can significantly improve patient care and are thus becoming increasingly popular. The data collected during randomized clinical trials are often used to estimate the optimal ITRs. However, these trials are generally expensive to run, and, moreover, they are not designed to efficiently estimate ITRs. In this paper, we propose a cost-effective estimation method from an active learning perspective. In particular, our method recruits only the "most informative" patients (in terms of learning the optimal ITRs) from an ongoing clinical trial. Simulation studies and real-data examples show that our active clinical trial method significantly improves on competing methods. We derive risk bounds and show that they support these observed empirical advantages.Comment: 48 Page, 9 Figures. To Appear in JASA--T&

The unified protocol for transdiagnostic treatment of emotional disorders compared with diagnosis-specific protocols for anxiety disorders a randomized clinical trial

IMPORTANCE: Transdiagnostic interventions have been developed to address barriers to the dissemination of evidence-based psychological treatments, but only a few preliminary studies have compared these approaches with existing evidence-based psychological treatments. OBJECTIVE: To determine whether the Unified Protocol for Transdiagnostic Treatment of Emotional Disorders (UP) is at least as efficacious as single-disorder protocols (SDPs) in the treatment of anxiety disorders. DESIGN, SETTING, AND PARTICIPANTS: From June 23, 2011, to March 5, 2015, a total of 223 patients at an outpatient treatment center with a principal diagnosis of panic disorder with or without agoraphobia, generalized anxiety disorder, obsessive-compulsive disorder, or social anxiety disorder were randomly assigned by principal diagnosis to the UP, an SDP, or a waitlist control condition. Patients received up to 16 sessions of the UP or an SDP for 16 to 21 weeks. Outcomes were assessed at baseline, after treatment, and at 6-month follow-up. Analysis in this equivalence trial was based on intention to treat. INTERVENTIONS: The UP or SDPs. MAIN OUTCOMES AND MEASURES: Blinded evaluations of principal diagnosis clinical severity rating were used to evaluate an a priori hypothesis of equivalence between the UP and SDPs. RESULTS: Among the 223 patients (124 women and 99 men; mean [SD] age, 31.1 [11.0] years), 88 were randomized to receive the UP, 91 to receive an SDP, and 44 to the waitlist control condition. Patients were more likely to complete treatment with the UP than with SDPs (odds ratio, 3.11; 95% CI, 1.44-6.74). Both the UP (Cohen d, −0.93; 95% CI, −1.29 to −0.57) and SDPs (Cohen d, −1.08; 95% CI, −1.43 to −0.73) were superior to the waitlist control condition at acute outcome. Reductions in clinical severity rating from baseline to the end of treatment (β, 0.25; 95% CI, −0.26 to 0.75) and from baseline to the 6-month follow-up (β, 0.16; 95% CI, −0.39 to 0.70) indicated statistical equivalence between the UP and SDPs. CONCLUSIONS AND RELEVANCE: The UP produces symptom reduction equivalent to criterion standard evidence-based psychological treatments for anxiety disorders with less attrition. Thus, it may be possible to use 1 protocol instead of multiple SDPs to more efficiently treat the most commonly occurring anxiety and depressive disorders.This study was funded by grant R01 MH090053 from the National Institute of Mental Health. (R01 MH090053 - National Institute of Mental Health)First author draf