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

Breast cancer screening is one of the most common radiological tasks withover 39 million exams performed each year. While breast cancer screening hasbeen one of the most studied medical imaging applications of artificialintelligence, the development and evaluation of the algorithms are hindered dueto the lack of well-annotated large-scale publicly available datasets. This isparticularly an issue for digital breast tomosynthesis (DBT) which is arelatively new breast cancer screening modality. We have curated and madepublicly available a large-scale dataset of digital breast tomosynthesisimages. It contains 22,032 reconstructed DBT volumes belonging to 5,610 studiesfrom 5,060 patients. This included four groups: (1) 5,129 normal studies, (2)280 studies where additional imaging was needed but no biopsy was performed,(3) 112 benign biopsied studies, and (4) 89 studies with cancer. Our datasetincluded masses and architectural distortions which were annotated by twoexperienced radiologists. Additionally, we developed a single-phase deeplearning detection model and tested it using our dataset to serve as a baselinefor future research. Our model reached a sensitivity of 65% at 2 falsepositives per breast. Our large, diverse, and highly-curated dataset willfacilitate development and evaluation of AI algorithms for breast cancerscreening through providing data for training as well as common set of casesfor model validation. The performance of the model developed in our study showsthat the task remains challenging and will serve as a baseline for future modeldevelopment.

jama network open

Detection of masses and architectural distortions in digital breast  tomosynthesis: a publicly available dataset of 5,060 patients and a deep  learning model