Following unprecedented success on the natural language tasks, Transformers have been
successfully applied to several computer vision problems, achieving state-of-the-art results …

Transformer, one of the latest technological advances of deep learning, has gained
prevalence in natural language processing or computer vision. Since medical imaging bear …

A large-scale and well-annotated dataset is a key factor for the success of deep learning in
medical image analysis. However, assembling such large annotations is very challenging …

Quantitative evaluation of tissue images is crucial for computational pathology (CPath) tasks,
requiring the objective characterization of histopathological entities from whole-slide images …

Benefiting from the large-scale archiving of digitized whole-slide images (WSIs), computer-
aided diagnosis has been well developed to assist pathologists in decision-making. Content …

Deep learning (DL) can accelerate the prediction of prognostic biomarkers from routine
pathology slides in colorectal cancer (CRC). However, current approaches rely on …

Liver and liver tumor segmentation from 3D volumetric images has been an active research
area in the medical image processing domain for the last few decades. The existence of …

Thermography images are a helpful screening tool that can detect breast cancer by showing
the body parts that indicate an abnormal change in temperature. Various segmentation …

Abstract Coronavirus disease 2019 (COVID-19) is pervasive worldwide, posing a high risk to
people's safety and health. Many algorithms were developed to identify COVID-19. One way …

Examination of pathological images is the golden standard for diagnosing and screening
many kinds of cancers. Multiple datasets, benchmarks, and challenges have been released …