Motivation While deep-learning algorithms have demonstrated outstanding performance in semantic image segmentation tasks, large annotation datasets are needed to create accurate models. Annotation of histology images is challenging due to the effort and experience required to carefully delineate tissue structures, and difficulties related to sharing and markup of whole-slide images. Results We recruited 25 participants, ranging in experience from senior pathologists to medical students, to delineate tissue regions in 151 breast cancer slides using the Digital Slide Archive. Inter-participant discordance was systematically evaluated, revealing low discordance for tumor and stroma, and higher discordance for more subjectively defined or rare tissue classes. Feedback provided by senior participants enabled the generation and curation of 20 000+ annotated tissue regions. Fully convolutional networks trained using these annotations were highly accurate (mean AUC=0.945), and the scale of annotation data provided notable improvements in image classification accuracy. Availability and Implementation Dataset is freely available at: https://goo.gl/cNM4EL. Supplementary information Supplementary data are available at Bioinformatics online.

Tissue based cancer studies can generate large amounts of histology data in the form of glass slides. These slides contain important diagnostic, prognostic, and biological information, and can be digitized into expansive and high-resolution whole-slide images (WSI) using slide-scanning devices. Effectively utilizing digital pathology data in cancer research requires the ability to manage, visualize, share and perform quantitative analysis on these large amounts of image data, tasks that are often complex and difficult for investigators with the current state of commercial digital pathology software. In this paper we describe the Digital Slide Archive (DSA), an open source web-based platform for digital pathology. DSA allows investigators to manage large collections of histologic images and integrate them with clinical and genomic metadata. The open-source model enables DSA to be extended to provide additional capabilities.

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In 2006, the latest version of a national curriculum for the fourth-year emergency medicine (EM) clerkship was published. Over the past several years, that curriculum has been implemented across multiple clerkships. The previous curriculum was found to be too long and detailed to cover in 4 weeks. As well, updates to the Liaison Committee on Medical Education (LCME)'s form and function document, which guides the structure of a clerkship, have occurred. Combining experience, updated guidelines, and the collective wisdom of members of the national organization of the Clerkship Directors in Emergency Medicine (CDEM), an update and revision of the fourth-year EM clerkship educational syllabi has been developed.

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