Nurses\u27 Alumnae Association Bulletin - Volume 6 Number 9

Remember the Relief Fund Welcome! Miss Childs Financial Report Calendar of Coming Events Lest You Forget! Attention Review of the Alumnae Association Meetings Institutional Staff Nurses\u27 Section Report of Staff Activities - 1947-1948 Private Duty Section The White Haven Division Barton Memorial Division Remember the Relief Fund Student Nurses\u27 Activities Jefferson Scores Again The Clara Melville Scholarship Fund Interesting Activities of the Nurses\u27 Home Committee of the Women\u27s Board Exclusive for Nurses Changes in the Maternity Division Gray Lady Musical Therapy Service Memorial Service Honoring Mrs. Bessie Dobson Altemus The Blood Donor Center The Hospital Pharmacy Medical College News Remember the Relief Fund Administrative Staff and Faculty of the School of Nursing Streptomycin Changes in the Staff at Jefferson Hospital Care of the Thoracic Surgical Patient Miscellaneous Items Marriages New Arrivals Deaths The Bulletin Committee Attention, Alumnae New Addresse

A publicly available deep learning model and dataset for segmentation of breast, fibroglandular tissue, and vessels in breast MRI

Abstract Breast density, or the amount of fibroglandular tissue (FGT) relative to the overall breast volume, increases the risk of developing breast cancer. Although previous studies have utilized deep learning to assess breast density, the limited public availability of data and quantitative tools hinders the development of better assessment tools. Our objective was to (1) create and share a large dataset of pixel-wise annotations according to well-defined criteria, and (2) develop, evaluate, and share an automated segmentation method for breast, FGT, and blood vessels using convolutional neural networks. We used the Duke Breast Cancer MRI dataset to randomly select 100 MRI studies and manually annotated the breast, FGT, and blood vessels for each study. Model performance was evaluated using the Dice similarity coefficient (DSC). The model achieved DSC values of 0.92 for breast, 0.86 for FGT, and 0.65 for blood vessels on the test set. The correlation between our model’s predicted breast density and the manually generated masks was 0.95. The correlation between the predicted breast density and qualitative radiologist assessment was 0.75. Our automated models can accurately segment breast, FGT, and blood vessels using pre-contrast breast MRI data. The data and the models were made publicly available

Island Visual Artists '86 : An Exhibition Representing the Work of Island Visual Artists

Wyatt stresses the importance of art education in Prince Edward Island. Includes brief statements and biographical notes for 93 artists