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

Deep learning methods such as convolutional neural networks (CNNs) aredifficult to directly utilize to analyze whole slide images (WSIs) due to thelarge image dimensions. We overcome this limitation by proposing a noveltwo-stage approach. First, we extract a set of representative patches (calledmosaic) from a WSI. Each patch of a mosaic is encoded to a feature vector usinga deep network. The feature extractor model is fine-tuned using hierarchicaltarget labels of WSIs, i.e., anatomic site and primary diagnosis. In the secondstage, a set of encoded patch-level features from a WSI is used to compute theprimary diagnosis probability through the proposed Pay Attention with Focusscheme, an attention-weighted averaging of predicted probabilities for allpatches of a mosaic modulated by a trainable focal factor. Experimental resultsshow that the proposed model can be robust, and effective for theclassification of WSIs.

Pay Attention with Focus: A Novel Learning Scheme for Classification of Whole Slide Images