RetinaDA unites six public fundus sets into a 512 × 512 macula-centered benchmark with built-in domain gaps, enabling ...

A powerful Visit Type Segmentation coupled with a traditional Recency, Frequency, Success model doesn’t just expose the data in a performance-efficient manner – it helps marketers understand how the ...

Accurate segmentation of medical images is essential for clinical decision-making, and deep learning techniques have shown remarkable results in this area. However, existing segmentation models that ...

Identifying and delineating cell structures in microscopy images is crucial for understanding the complex processes of life. This task is called "segmentation" and it enables a range of applications, ...