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 …

Abstract Machine learning techniques have great potential to improve medical diagnostics,
offering ways to improve accuracy, reproducibility and speed, and to ease workloads for …

Digital pathology poses unique computational challenges, as a standard gigapixel slide may
comprise tens of thousands of image tiles,–. Prior models have often resorted to …

Cancer is one of the most dangerous diseases to humans, and yet no permanent cure has
been developed for it. Breast cancer is one of the most common cancer types. According to …

Breast cancer is one of the precarious conditions that affect women, and a substantive cure
has not yet been discovered for it. With the advent of Artificial intelligence (AI), recently, deep …

For machine learning systems to be reliable, we must understand their performance in
unseen, out-of-distribution environments. In this paper, we empirically show that out-of …

Abstract The Cancer Genome Atlas (TCGA) is one of the largest biorepositories of digital
histology. Deep learning (DL) models have been trained on TCGA to predict numerous …

Histopathological images contain rich phenotypic information that can be used to monitor
underlying mechanisms contributing to disease progression and patient survival outcomes …

Distribution shifts—where the training distribution differs from the test distribution—can
substantially degrade the accuracy of machine learning (ML) systems deployed in the wild …

With the development of Computer-aided Diagnosis (CAD) and image scanning techniques,
Whole-slide Image (WSI) scanners are widely used in the field of pathological diagnosis …