In modern clinical practice, digital pathology has a crucial role and is increasingly a
technological requirement in the scientific laboratory environment. The advent of whole-slide …

Recently, deep learning frameworks have rapidly become the main methodology for
analyzing medical images. Due to their powerful learning ability and advantages in dealing …

Advances in digitizing tissue slides and the fast-paced progress in artificial intelligence,
including deep learning, have boosted the field of computational pathology. This field holds …

Tumor proliferation is an important biomarker indicative of the prognosis of breast cancer
patients. Assessment of tumor proliferation in a clinical setting is a highly subjective and …

Background Deep learning has the potential to transform health care; however, substantial
expertise is required to train such models. We sought to evaluate the utility of automated …

Deep learning (DL) has achieved state-of-the-art performance in many digital pathology
analysis tasks. Traditional methods usually require hand-crafted domain-specific features …

Breast cancer is the leading cause of mortality in women. Early diagnosis of breast cancer
can reduce the mortality rate. In the diagnosis, the mitotic cell count is an important …

The mitotic activity index is a key prognostic measure in tumour grading. Microscopy based
detection of mitotic nuclei is a significant overhead and necessitates automation. This work …

Technical advancements significantly improve earlier diagnosis of cervical cancer, but
accurate diagnosis is still difficult due to various factors. We develop an artificial intelligence …

Developing new deep learning methods for medical image analysis is a prevalent research
topic in machine learning. In this paper, we propose a deep learning scheme with a novel …