Learning to Segment Breast Biopsy Whole Slide Images

We trained and applied an encoder-decoder model to semantically segment breast biopsy images into biologically meaningful tissue labels. Since conventional encoder-decoder networks cannot be applied directly on large biopsy images and the different sized structures in biopsies present novel challenges, we propose four modifications: (1) an input-aware encoding block to compensate for information loss, (2) a new dense connection pattern between encoder and decoder, (3) dense and sparse decoders to combine multi-level features, (4) a multi-resolution network that fuses the results of encoder-decoders run on different resolutions. Our model outperforms a feature-based approach and conventional encoder-decoders from the literature. We use semantic segmentations produced with our model in an automated diagnosis task and obtain higher accuracies than a baseline approach that employs an SVM for feature-based segmentation, both using the same segmentation-based diagnostic features.Comment: Added more WSI images in appendi

DeepSolarEye: Power Loss Prediction and Weakly Supervised Soiling Localization via Fully Convolutional Networks for Solar Panels

The impact of soiling on solar panels is an important and well-studied problem in renewable energy sector. In this paper, we present the first convolutional neural network (CNN) based approach for solar panel soiling and defect analysis. Our approach takes an RGB image of solar panel and environmental factors as inputs to predict power loss, soiling localization, and soiling type. In computer vision, localization is a complex task which typically requires manually labeled training data such as bounding boxes or segmentation masks. Our proposed approach consists of specialized four stages which completely avoids localization ground truth and only needs panel images with power loss labels for training. The region of impact area obtained from the predicted localization masks are classified into soiling types using the webly supervised learning. For improving localization capabilities of CNNs, we introduce a novel bi-directional input-aware fusion (BiDIAF) block that reinforces the input at different levels of CNN to learn input-specific feature maps. Our empirical study shows that BiDIAF improves the power loss prediction accuracy by about 3% and localization accuracy by about 4%. Our end-to-end model yields further improvement of about 24% on localization when learned in a weakly supervised manner. Our approach is generalizable and showed promising results on web crawled solar panel images. Our system has a frame rate of 22 fps (including all steps) on a NVIDIA TitanX GPU. Additionally, we collected first of it's kind dataset for solar panel image analysis consisting 45,000+ images.Comment: Accepted for publication at WACV 201

Relationship between Socio-Demographic Factors and Familial and Partner Pressures to Conceive in HIV-Positive Women in Ontario

This study examined the relationship between socio-demographic factors and family and partner pressure to conceive in women living with HIV in Ontario, Canada. A total of 490 women, aged 18-52 years were included in the study. The HIV Pregnancy Planning Questionnaire was used to collect data on socio-demographic, medical, and pressure variables. Multivariate logistic regression analysis suggest that increased age, years lived in Canada, and living in Toronto were associated with lower odds, and being married and having 0-1 lifetime births were associated with higher odds of family pressure to conceive. Increased age was associated with lower odds, and being married and living in Toronto were associated with higher odds of partner pressure to conceive. Findings suggest that socio-demographic factors influence the fertility decision-making process. Health care providers should consider socio-demographic factors along with medical factors when assisting women living with HIV and their partners to make informed reproductive decisions

An Empirical Analysis on Relationship Between Current Account, Capital Account and Gross Domestic Product in India

This paper examines the link between Current Account, Capital Account and GDP using pairwise Granger Causality Test. This study analyzed the trend and pattern of balance of payment during the before and after devaluation period. It is furthermore assess impact of devaluation on balance of payment using paired sample‘t’ test. The result exposed that the one way causality rerunning from GDP to Capital Account. We also found that one way causality rerunning from Current Account to GDP. The result indicates that there is significant improvement in balance of payments during the pre- to post-devaluation period