English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

Creating a dataset for training supervised machine learning algorithms can bea demanding task. This is especially true for medical image segmentation sinceone or more specialists are usually required for image annotation, and creatingground truth labels for just a single image can take up to several hours. Inaddition, it is paramount that the annotated samples represent well thedifferent conditions that might affect the imaged tissues as well as possiblechanges in the image acquisition process. This can only be achieved byconsidering samples that are typical in the dataset as well as atypical, oreven outlier, samples. We introduce VessMAP, a heterogeneous blood vesselsegmentation dataset acquired by carefully sampling relevant images from alarger non-annotated dataset. A methodology was developed to select bothprototypical and atypical samples from the base dataset, thus defining anassorted set of images that can be used for measuring the performance ofsegmentation algorithms on samples that are highly distinct from each other. Todemonstrate the potential of the new dataset, we show that the validationperformance of a neural network changes significantly depending on the splitsused for training the network.

A new dataset for measuring the performance of blood vessel segmentation methods under distribution shifts