Ki-67 assessment is a key step in the diagnosis of neuroendocrine neoplasms (NENs) from
all anatomic locations. Several challenges exist related to quantifying the Ki-67 proliferation …

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

Artificial intelligence (AI), including deep learning methods that leverage neural network-
based algorithms, hold significant promise for dermatopathology and other areas of …

Deep learning has been widely used to analyze digitized hematoxylin and eosin (H&E)-
stained histopathology whole slide images. Automated cancer segmentation using deep …

Surgery combined with systemic chemotherapy is currently the main modality of cancer
treatment. However, the development of cancer multidrug resistance and inevitable surgical …

Background Machine learning (ML), a branch of artificial intelligence, involves the
development of algorithms focused on pattern recognition and learning for computer-based …

Breast cancer has become the most common form of cancer among women. In recent years,
deep learning has shown great potential in aiding the diagnosis of pathological images …

Pathologists diagnose prostate cancer by core needle biopsy. In low-grade and low-volume
cases, they look for a few malignant glands out of hundreds within a core. They may miss a …

Automatic skin lesion segmentation remains a challenging task due to particular
characteristics of the patient and various artifacts. In this paper, a novel network named …

Cancer subtyping is one of the most challenging tasks in digital pathology, where Multiple
Instance Learning (MIL) by processing gigapixel whole slide images (WSIs) has been in the …