Unsupervised domain adaptation aims to learn a model of classifier for unlabeled samples on the target domain, given training data of labeled samples on the source domain. Impressive progress is made recently by learning invariant features via domain-adversarial training of deep networks. In spite of the recent progress, domain adaptation is still limited in achieving the invariance of feature distributions at a finer category level. To this end, we propose in this paper a new domain adaptation method called Domain-Symmetric Networks (SymNets). The proposed SymNet is based on a symmetric design of source and target task classifiers, based on which we also construct an additional classifier that shares with them its layer neurons. To train the SymNet, we propose a novel adversarial learning objective whose key design is based on a two-level domain confusion scheme, where the category-level confusion loss improves over the domain-level one by driving the learning of intermediate network features to be invariant at the corresponding categories of the two domains. Both domain discrimination and domain confusion are implemented based on the constructed additional classifier. Since target samples are unlabeled, we also propose a scheme of cross-domain training to help learn the target classifier. Careful ablation studies show the efficacy of our proposed method. In particular, based on commonly used base networks, our SymNets achieve the new state of the art on three benchmark domain adaptation datasets.

With the introduction of fully convolutional neural networks, deep learning has raised the benchmark for medical image segmentation on both speed and accuracy, and different networks have been proposed for 2D and 3D segmentation with promising results. Nevertheless, most networks only handle relatively small numbers of labels (<10), and there are very limited works on handling highly unbalanced object sizes especially in 3D segmentation. In this paper, we propose a network architecture and the corresponding loss function which improve segmentation of very small structures. By combining skip connections and deep supervision with respect to the computational feasibility of 3D segmentation, we propose a fast converging and computationally efficient network architecture for accurate segmentation. Furthermore, inspired by the concept of focal loss, we propose an exponential logarithmic loss which balances the labels not only by their relative sizes but also by their segmentation difficulties. We achieve an average Dice coefficient of 82% on brain segmentation with 20 labels, with the ratio of the smallest to largest object sizes as 0.14%. Less than 100 epochs are required to reach such accuracy, and segmenting a 128×128×128 volume only takes around 0.4 s.

Unsupervised domain adaptation (UDA) is to learn classification models that make predictions for unlabeled data on a target domain, given labeled data on a source domain whose distribution diverges from the target one. Mainstream UDA methods strive to learn domain-aligned features such that classifiers trained on the source features can be readily applied to the target ones. Although impressive results have been achieved, these methods have a potential risk of damaging the intrinsic data structures of target discrimination, raising an issue of generalization particularly for UDA tasks in an inductive setting. To address this issue, we are motivated by a UDA assumption of structural similarity across domains, and propose to directly uncover the intrinsic target discrimination via constrained clustering, where we constrain the clustering solutions using structural source regularization that hinges on the very same assumption. Technically, we propose a hybrid model of Structurally Regularized Deep Clustering, which integrates the regularized discriminative clustering of target data with a generative one, and we thus term our method as SRDC++. Our hybrid model is based on a deep clustering framework that minimizes the Kullback-Leibler divergence between the distribution of network prediction and an auxiliary one, where we impose structural regularization by learning domain-shared classifier and cluster centroids. By enriching the structural similarity assumption, we are able to extend SRDC++ for a pixel-level UDA task of semantic segmentation. We conduct extensive experiments on seven UDA benchmarks of image classification and semantic segmentation. With no explicit feature alignment, our proposed SRDC++ outperforms all the existing methods under both the inductive and transductive settings. We make our implementation codes publicly available at https://github.com/huitangtang/SRDCPP.

Background We sought to determine whether perceived patient-centered medical home (PCMH) characteristics are associated with staff morale, job satisfaction, and burnout in safety net clinics. Methods Self-administered survey among 391 providers and 382 clinical staff across 65 safety net clinics in 5 states in 2010. The following 5 subscales measured respondents’ perceptions of PCMH characteristics on a scale of 0 to 100 (0 indicates worst and 100 indicates best): access to care and communication with patients, communication with other providers, tracking data, care management, and quality improvement. The PCMH sub-scale scores were averaged to create a total PCMH score. Results Six hundred three persons (78.0%) responded. In multivariate generalized estimating equation models, a 10% increase in the quality improvement subscale score was associated with higher morale (provider odds ratio [OR], 2.64; 95% CI, 1.47–4.75; staff OR, 3.62; 95% CI, 1.84–7.09), greater job satisfaction (provider OR, 2.45; 95% CI, 1.42–4.23; staff OR, 2.55; 95% CI 1.42–4.57), and freedom from burnout (staff OR, 2.32; 95% CI, 1.31–4.12). The total PCMH score was associated with higher staff morale (OR, 2.63; 95% CI, 1.47–4.71) and with lower provider freedom from burnout (OR, 0.48; 95% CI, 0.30–0.77). A separate work environment covariate correlated highly with the quality improvement subscale score and the total PCMH score, and PCMH characteristics had attenuated associations with morale and job satisfaction when included in models. Conclusions Providers and staff who perceived more PCMH characteristics in their clinics were more likely to have higher morale, but the providers had less freedom from burnout. Among the PCMH subscales, the quality improvement subscale score particularly correlated with higher morale, greater job satisfaction, and freedom from burnout.

New translational strategies are needed to improve diabetes outcomes among low-income African-Americans. Our goal was to develop/pilot test a patient intervention combining culturally tailored diabetes education with shared decision-making training. This was an observational cohort study. Surveys and clinical data were collected at baseline, program completion, and 3 and 6 months. There were 21 participants; the mean age was 61 years. Eighty-six percent of participants attended >70 % of classes. There were improvements in diabetes self-efficacy, self-care behaviors (i.e., following a "healthful eating plan" (mean score at baseline 3.4 vs. 5.2 at program's end; p = 0.002), self glucose monitoring (mean score at baseline 4.3 vs. 6.2 at program's end; p = 0.04), and foot care (mean score at baseline 4.1 vs. 6.0 at program's end; p = 0.001)), hemoglobin A1c (8.24 at baseline vs. 7.33 at 3-month follow-up, p = 0.02), and HDL cholesterol (51.2 at baseline vs. 61.8 at 6-month follow-up, p = 0.01). Combining tailored education with shared decision-making may be a promising strategy for empowering low-income African-Americans and improving health outcomes.

PURPOSE: 1) To pilot a health disparities curriculum for incoming first year medical students and evaluate changes in knowledge. 2) To help students become aware of personal biases regarding racial and ethnic minorities. 3) To inspire students to commit to serving indigent populations.METHODS: First year students participated in a 5-day elective course held before orientation week. The course used the curricular goals that had been developed by the Society of General Internal Medicine Health Disparities Task Force. Thirty-two faculty members from multiple institutions and different disciplinary backgrounds taught the course. Teaching modalities included didactic lectures, small group discussions, off-site expeditions to local free clinics, community hospitals and clinics, and student-led poster session workshops. The course was evaluated by pre-post surveys.RESULTS: Sixty-four students (60% of matriculating class) participated. Survey response rates were 97-100%. Students' factual knowledge (76 to 89%, p<.0009) about health disparities and abilities to address disparities issues improved after the course. This curriculum received the highest rating of any course at the medical school (overall mean 4.9, 1 = poor, 5 = excellent).CONCLUSIONS: This innovative course provided students an opportunity for learning and exploration of a comprehensive curriculum on health disparities at a critical formative time.

Background Self-reported racial discrimination in healthcare has been associated with negative health outcomes, but little is known about its association with diabetes outcomes. Methods We utilized data from the Behavioral Risk Factor Surveillance System to investigate associations between self-reported healthcare discrimination and the following diabetes outcomes: 1) quality of care, 2) self-management and 3) complications. Results In unadjusted logistic regression models, significant associations were found between self-reported healthcare discrimination and most measures of quality of care (diabetes-related primary care visits [OR: 0.38, 95% CI: 0.21, 0.66], HbA1c testing [OR: 0.42, 95%CI: 0.21, 0.82], and prior eye exam interval [OR: 0.48, 95% CI: 0.24, 0.93]) and health outcomes (foot disorders [OR: 2.32, 95%CI: 1.15, 4.68] and retinopathy [OR: 2.26, 95%CI: 1.24, 4.12]), but not the number of provider foot examinations (p=0.48) or diabetes self-management (self glucose monitoring, p=0.42; self foot examinations, p=0.74; diabetes class participation, p=0.37). The effects of self-reported discrimination were attenuated or eliminated after controlling for sociodemographics, health status, and access to care. Conclusions Self-reported racial/ethnic discrimination in healthcare was associated with worse diabetes care and more diabetes complications, but not self-care behaviors, suggesting that factors beyond patients’ own behaviors may be the main source of differential outcomes. The relationships between self-reported discrimination and diabetes outcomes were eliminated once adjusting for sociodemographics, health status, and access to care. Our findings suggest that other factors (i.e. race, insurance, health status) may play equally or more important roles in determining diabetes health disparities, and that a comprehensive strategy is needed to effectively address health disparities.

Stereochemistry is an important topic in organic chemistry. It is also a difficult topic for students to learn. This study investigated the relative effectiveness on students’ understanding of three kinds of molecular representations of stereochemistry concepts. Instructional activities compared the use of either: (i) computer-based molecular visualization software; (ii) handheld ball and stick models; or (iii) two-dimensional perspective drawings to represent stereochemical structures. Results showed that computer visualization software could be an effective tool for teaching stereochemistry.