Digital pathology has transformed the traditional pathology practice of analyzing tissue
under a microscope into a computer vision workflow. Whole slide imaging allows …

Quantitative evaluation of tissue images is crucial for computational pathology (CPath) tasks,
requiring the objective characterization of histopathological entities from whole-slide images …

Images of the natural world collected by a variety of cameras from drones to individual
phones are increasingly abundant sources of biological information. There is an explosion …

The development of robust artificial intelligence models for echocardiography has been
limited by the availability of annotated clinical data. Here, to address this challenge and …

Foundation model, which is pre-trained on broad data and is able to adapt to a wide range
of tasks, is advancing healthcare. It promotes the development of healthcare artificial …

Abstract Representation learning of pathology whole-slide images (WSIs) has been has
primarily relied on weak supervision with Multiple Instance Learning (MIL). However the …

Tissue phenotyping is a fundamental computational pathology (CPath) task in learning
objective characterizations of histopathologic biomarkers in anatomic pathology. However …

Self-supervised learning (SSL) has been successful in building patch embeddings of small
histology images (eg 224 x 224 pixels) but scaling these models to learn slide embeddings …

The field of computational pathology has witnessed remarkable progress in the
development of both task-specific predictive models and task-agnostic self-supervised vision …

Abstract Whole Slide Image (WSI) classification is often formulated as a Multiple Instance
Learning (MIL) problem. Recently Vision-Language Models (VLMs) have demonstrated …