Over the last decade, supervised deep learning on manually annotated big data has been
progressing significantly on computer vision tasks. But, the application of deep learning in …

Digital histopathological images, high‐resolution images of stained tissue samples, are a
vital tool for clinicians to diagnose and stage cancers. The visual analysis of patient state …

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

In recent years, with the advancement of computer-aided diagnosis (CAD) technology and
whole slide image (WSI), histopathological WSI has gradually played a crucial aspect in the …

One of the most common diseases affecting society around the world is kidney tumor. The
risk of kidney disease increases due to reasons such as consumption of ready-made food …

Simple Summary Recent AI methods in the automated analysis of histopathological imaging
data associated with cancer have trended towards less supervision by humans. Yet, there …

Pathological examination is the optimal approach for diagnosing cancer, and with the
advancement of digital imaging technologies, it has spurred the emergence of …

Molecular and genomic properties are critical in selecting cancer treatments to target
individual tumors, particularly for immunotherapy. However, the methods to assess such …

Deep learning models have exhibited exceptional effectiveness in Computational Pathology
(CPath) by tackling intricate tasks across an array of histology image analysis applications …

Searching for similar images in archives of histology and histopathology images is a crucial
task that may aid in patient tissue comparison for various purposes, ranging from triaging …