Background Histological assessment of colorectal cancer (CRC) tissue is a crucial and
demanding task for pathologists. Unfortunately, manual annotation by trained specialists is a …

Nuclei identification is a fundamental task in many areas of biomedical image analysis
related to computational pathology applications. Nowadays, deep learning is the primary …

Liver segmentation is a crucial step in surgical planning from computed tomography scans.
The possibility to obtain a precise delineation of the liver boundaries with the exploitation of …

The segmentation and classification of cell nuclei are pivotal steps in the pipelines for the
analysis of bioimages. Deep learning (DL) approaches are leading the digital pathology …

The COVID-19 pandemic is inevitably changing the world in a dramatic way, and the role of
computed tomography (CT) scans can be pivotal for the prognosis of COVID-19 patients …

Computer-aided classification of diseases of the gastrointestinal tract (GIT) has become a
crucial area of research. Medical science and artificial intelligence have helped medical …

The complex pathobiology of lung cancer, and its spread worldwide, has prompted research
studies that combine radiomic and genomic approaches. Indeed, the early identification of …

Purpose: Automatic outlining of different tissue types in digitized histological specimen
provides a basis for follow-up analyses and can potentially guide subsequent medical …

The Third most prevalent cause of cancer death in the world is colorectal lymphomas (CL)
The lymphomas Volume is usually estimated using Magnetic Resonance imaging (MRI) …

Globally, colorectal cancer (CRC) is a major source of illness and death. Increasing early
detection is essential to bettering patient outcomes. Transfer learning has been a viable …