Pathology is the cornerstone of cancer care. The need for accuracy in histopathologic
diagnosis of cancer is increasing as personalized cancer therapy requires accurate …

Abstract Computational Pathology (CPath) is an interdisciplinary science that augments
developments of computational approaches to analyze and model medical histopathology …

Importance Deep learning–based methods, such as the sliding window approach for
cropped-image classification and heuristic aggregation for whole-slide inference, for …

Applying curriculum learning requires both a range of difficulty in data and a method for
determining the difficulty of examples. In many tasks, however, satisfying these requirements …

We developed end-to-end deep learning models using whole slide images of adults
diagnosed with diffusely infiltrating, World Health Organization (WHO) grade 2 gliomas to …

Renal cell carcinoma (RCC) is the most common renal cancer in adults. The histopathologic
classification of RCC is essential for diagnosis, prognosis, and management of patients …

With the rise of deep learning, there has been increased interest in using neural networks for
histopathology image analysis, a field that investigates the properties of biopsy or resected …

Celiac disease (CD) is a gluten-sensitive immune-mediated enteropathy. This proof-of-
concept study used a convolutional neural network (CNN) to classify hematoxylin and eosin …

Digital pathology had a recent growth, stimulated by the implementation of digital whole
slide images (WSIs) in clinical practice, and the pathology field faces shortage of …

Cancers are the leading cause of death in many countries. Early diagnosis plays a crucial
role in having proper treatment for this debilitating disease. The automated classification of …