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 …

Simple Summary The histopathological image is widely considered as the gold standard for
the diagnosis and prognosis of human cancers. Recently, deep learning technology has …

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

Predicting distant recurrence of endometrial cancer (EC) is crucial for personalized adjuvant
treatment. The current gold standard of combined pathological and molecular profiling is …

Methods of computational pathology applied to the analysis of whole-slide images (WSIs) do
not typically consider histopathological features from the tumour microenvironment. Here …

Graph-based methods have been extensively applied to whole slide histopathology image
(WSI) analysis due to the advantage of modeling the spatial relationships among different …

The adoption of digital pathology has enabled the curation of large repositories of gigapixel
whole-slide images (WSIs). Computationally identifying WSIs with similar morphologic …

Histopathological whole slide images (WSIs) classification has become a foundation task in
medical microscopic imaging processing. Prevailing approaches involve learning WSIs as …

Deep learning is a powerful tool for whole slide image (WSI) analysis. Typically, when
performing supervised deep learning, a WSI is divided into small patches, trained and the …

Gastric cancer and Colon adenocarcinoma represent widespread and challenging
malignancies with high mortality rates and complex treatment landscapes. In response to the …