Single nucleus segmentation is a frequent challenge of microscopy image processing, since
it is the first step of many quantitative data analysis pipelines. The quality of tracking single …

Deep learning (DL) has become one of the major approaches in computational
dermatopathology, evidenced by a significant increase in this topic in the current literature …

In common medical procedures, the time-consuming and expensive nature of obtaining test
results plagues doctors and patients. Digital pathology research allows using computational …

Microscopy images are rich in information about the dynamic relationships among biological
structures. However, extracting this complex information can be challenging, especially …

Background Image-based machine learning tools hold great promise for clinical applications
in pathology research. However, the ideal end-users of these computational tools (eg …

Advancements in digital pathology and computing resources have made a significant impact
in the field of computational pathology for breast cancer diagnosis and treatment. However …

Advanced image analysis with machine and deep learning has improved cell segmentation
and classification for novel insights into biological mechanisms. These approaches have …

Active Learning (AL) has the potential to solve a major problem of digital pathology: the
efficient acquisition of labeled data for machine learning algorithms. However, existing AL …

The parallel development of digital and telecommunication technologies in recent years has
created ample opportunities to improve traditional healthcare delivery systems. New models …

Abstract Recent advances in High Performance Computing (HPC) enable Deep Learning
(DL) models to achieve state-of-the-art performance by exploiting multiple processors. Data …