Abstract Vision Transformers (ViTs), with the magnificent potential to unravel the information
contained within images, have evolved as one of the most contemporary and dominant …

Deep learning has emerged as a leading machine learning tool in object detection and has
attracted attention with its achievements in progressing medical image analysis …

Abstract Vision Transformers (ViTs) and their multi-scale and hierarchical variations have
been successful at capturing image representations but their use has been generally …

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 …

Contrastive visual language pretraining has emerged as a powerful method for either
training new language-aware image encoders or augmenting existing pretrained models …

Deep-learning methods for computational pathology require either manual annotation of
gigapixel whole-slide images (WSIs) or large datasets of WSIs with slide-level labels and …

Nuclear segmentation and classification within Haematoxylin & Eosin stained histology
images is a fundamental prerequisite in the digital pathology work-flow. The development of …

Deep Learning-based computational pathology algorithms have demonstrated profound
ability to excel in a wide array of tasks that range from characterization of well known …

With the remarkable success of representation learning for prediction problems, we have
witnessed a rapid expansion of the use of machine learning and deep learning for the …

In this work we present an experimental setup to semi automatically obtain exhaustive nuclei
labels across 19 different tissue types, and therefore construct a large pan-cancer dataset for …