Anatomy-specific classification of medical images using deep convolutional nets

Automated classification of human anatomy is an important prerequisite for many computer-aided diagnosis systems. The spatial complexity and variability of anatomy throughout the human body makes classification difficult. "Deep learning" methods such as convolutional networks (ConvNets) outperform other state-of-the-art methods in image classification tasks. In this work, we present a method for organ- or body-part-specific anatomical classification of medical images acquired using computed tomography (CT) with ConvNets. We train a ConvNet, using 4,298 separate axial 2D key-images to learn 5 anatomical classes. Key-images were mined from a hospital PACS archive, using a set of 1,675 patients. We show that a data augmentation approach can help to enrich the data set and improve classification performance. Using ConvNets and data augmentation, we achieve anatomy-specific classification error of 5.9 % and area-under-the-curve (AUC) values of an average of 0.998 in testing. We demonstrate that deep learning can be used to train very reliable and accurate classifiers that could initialize further computer-aided diagnosis.Comment: Presented at: 2015 IEEE International Symposium on Biomedical Imaging, April 16-19, 2015, New York Marriott at Brooklyn Bridge, NY, US

Interleaved text/image Deep Mining on a large-scale radiology database

Despite tremendous progress in computer vision, effec-tive learning on very large-scale (> 100K patients) medi-cal image databases has been vastly hindered. We present an interleaved text/image deep learning system to extract and mine the semantic interactions of radiology images and reports from a national research hospital’s picture archiv-ing and communication system. Instead of using full 3D medical volumes, we focus on a collection of representa-tive ~216K 2D key images/slices (selected by clinicians for diagnostic reference) with text-driven scalar and vector la-bels. Our system interleaves between unsupervised learn-ing (e.g., latent Dirichlet allocation, recurrent neural net language models) on document- and sentence-level texts to generate semantic labels and supervised learning via deep convolutional neural networks (CNNs) to map from images to label spaces. Disease-related key words can be predicted for radiology images in a retrieval manner. We have demon-strated promising quantitative and qualitative results. The large-scale datasets of extracted key images and their cat-egorization, embedded vector labels and sentence descrip-tions can be harnessed to alleviate the deep learning “data-hungry ” obstacle in the medical domain

2D View Aggregation for Lymph Node Detection Using a Shallow Hierarchy of Linear Classifiers

Enlarged lymph nodes (LNs) can provide important information for cancer diagnosis, staging, and measuring treatment reactions, making automated detection a highly sought goal. In this paper, we propose a new algorithm representation of decomposing the LN detection problem into a set of 2D object detection subtasks on sampled CT slices, largely alleviating the curse of dimensionality issue. Our 2D detection can be effectively formulated as linear classification on a single image feature type of Histogram of Oriented Gradients (HOG), covering a moderate field-of-view of 45 by 45 voxels. We exploit both simple pooling and sparse linear fusion schemes to aggregate these 2D detection scores for the final 3D LN detection. In this manner, detection is more tractable and does not need to perform perfectly at instance level (as weak hypotheses) since our aggregation process will robustly harness collective information for LN detection. Two datasets (90 patients with 389 mediastinal LNs and 86 patients with 595 abdominal LNs) are used for validation. Cross-validation demonstrates 78.0% sensitivity at 6 false positives/volume (FP/vol.) (86.1% at 10 FP/vol.) and 73.1% sensitivity at 6 FP/vol. (87.2% at 10 FP/vol.), for the mediastinal and abdominal datasets respectively. Our results compare favorably to previous state-of-the-art methods.Comment: This article will be presented at MICCAI (Medical Image Computing and Computer-Assisted Intervention) 201

Perpetration of intimate partner violence and mental health outcomes: sex- and gender-disaggregated associations among adolescents and young adults in Nigeria.

BACKGROUND: The association between intimate partner violence (IPV) victimisation and poor mental health outcomes is well established. Less is known about the correlation between IPV perpetration and mental health, particularly among adolescents and young adults. Using data from the nationally representative Violence Against Children Survey, this analysis examines the association between IPV perpetration and mental health for male and female adolescents and young adults in Nigeria. METHODS: Multivariate logistic regression models were used to examine associations between ever-perpetration of IPV and four self-reported mental health variables: severe sadness, feelings of worthlessness, suicide ideation, and alcohol use. Models were sex-disaggregated, controlled for age, marital status, and schooling, and tested with and without past exposure to violence. Standard errors were adjusted for sampling stratification and clustering. Observations were weighted to be representative of 13-24 year-olds in Nigeria. RESULTS: Males were nearly twice as likely as females to perpetrate IPV (9% v. 5%, respectively; P < 0.001), while odds of perpetration for both sexes were higher for those ever experiencing IPV (adjusted odds ratio (aOR) = 4.60 for males; aOR = 2.71 for females). Female perpetrators had 2.73 higher odds of reporting severe sadness (95% confidence interval CI = 1.44, 5.17; P = 0.002) and 2.72 times greater odds of reporting suicide ideation (1.28, 5.79; P = 0.010) than non-perpetrating females, even when controlling for past-year violence victimisation. In contrast, male perpetrators had 2.65 times greater odds of feeling worthless (1.09, 6.43; P = 0.031), and 2.36 times greater odds of reporting alcohol use in the last 30 days (1.50, 3.73; P < 0.001), as compared to non-perpetrating males. CONCLUSIONS: Among adolescents and young adults in Nigeria, IPV perpetration and negative mental health outcomes are associated but differ for males and females. Mindful of the cross-sectional nature of the data, it is possible that socially determined gender norms may shape the ways in which distress from IPV perpetration is understood and expressed. Additional research is needed to clarify these associations and inform violence prevention efforts

Innovative methods to analyse the impact of gender norms on adolescent health using global health survey data.

BACKGROUND: Understanding how gender norms affect health is an important entry point into designing programs and policies to change norms and improve gender equality and health. However, it is rare for global health datasets to include questions on gender norms, especially questions that go beyond measuring gender-related attitudes, thus limiting gender analysis. METHODS: We developed five case studies using health survey data from six countries to demonstrate approaches to defining and operationalising proxy measures and analytic approaches to investigating how gender norms can affect health. Key findings, strengths and limitations of our norms proxies and methodological choices are summarised. FINDINGS: Case studies revealed links between gender norms and multiple adolescent health outcomes. Proxys for norms were derived from data on attitudes, beliefs, and behaviours, as well as differences between attitudes and behaviours. Data were cross-sectional, longitudinal, census- and social network-based. Analytic methods were diverse. We found that gender norms affect: 1) Intimate partner violence in Nigeria; 2) Unhealthy weight control behaviours in Brazil and South Africa; 3) HIV status in Zambia; 4) Health and social mobility in the US; and 5) Childbirth in Honduras. INTERPRETATION: Researchers can use existing global health survey data to examine pathways through which gender norms affect health by generating proxies for gender norms. While direct measures of gender norms can greatly improve the understanding of how gender affects health, proxy measures for norms can be designed for the specific health-related outcome and normative context, for instance by either aggregating behaviours or attitudes or quantifying the difference (dissonance) between them. These norm proxies enable evaluations of the influence of gender norms on health and insights into possible reference groups and sanctions for non-compliers, thus informing programmes and policies to shape norms and improve health

Complete redox exchange of indium for Tl+ in zeolite A. Crystal structures of anhydrous Tl-12-A and In-10-A center dot In. Indium appears as In2+, In+, and In-0. The clusters(In-5)(8+) and (In-3)(2+) are proposed

Indium has replaced all of the Tl+ ions in fully dehydrated fully Tl+(-)exchanged zeolite A by a solvent-free redox ion-exchange reaction with In metal at 623 K. The crystal structures of the zeolite before (Tl12Si12-Al12O48: a = 12.153(4) Angstrom, R-1 = 0.054, and R-2 = 0.060) and after the reaction, followed by washing with water and redehydration at 623 K (In10Si12Al12O48. In: a = 12.098(2) Angstrom, R-1 = 0.063, and R-2 = 0.062), have been determined by single-crystal X-ray crystallography at 294 K using the space group Pm3m. In In-10-A . In, 11 In atoms or ions/unit cell are distributed over seven crystallographically distinct positions. Seven In ions occupy 3-fold-axis equipoints: four In+ ions lie opposite 6-rings in large cavity (In(1)), and two In2+ (In(2)) and one In+ (In(3)) lie opposite 6-rings in the sodalite unit. Three In+ ions per unit cell are found at two different 8-ring positions: 1.5 on the 8-ring plane (In(4)) and 1.5 off(ln(5)). Finally, one In-0 atom per unit cell, probably associated with In ions, is found at two quite unusual positions: one-half of an In-0 lies at the center of sodalite unit (In(6)) and the other half of the In-0 is opposite a 4-ring relatively deep in the large cavity (In(7)). The crystal structure of In-10-A . In is viewed as a mixture of two kinds of &apos;&apos;unit cells,&apos;&apos; In-8-A . In and In-12-A . In, each with a cationic charge of 12+. By their approach distances to framework oxygens, the ionic radii of In+ and In2+ are ca. 1.23 and 1.04 Angstrom, respectively. The In(6) and In(7) positions lie deep within cavities where they approach only In cations. This suggests the existence of tetrahedral (In-5)(8+) clusters (four In2+ ions at In(2) with an In-0 atom at their center at In(6), In(2)-In(6) distance = 2.754(2) Angstrom in half of the sodalite units (In-8-A . In), and bent (In-3)(2+) clusters In(1)-In(7)-In(1) angle = 148.0(9)(0) and In(1)-In(7) = 3.073(8) Angstrom) in half of the large cavities (In-12-A . In).X1126sciescopu