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Artificial intelligence for digital and computational pathology
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 …
including deep learning, have boosted the field of computational pathology. This field holds …
Recent advances of deep learning for computational histopathology: principles and applications
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 …
the diagnosis and prognosis of human cancers. Recently, deep learning technology has …
Visual language pretrained multiple instance zero-shot transfer for histopathology images
Contrastive visual language pretraining has emerged as a powerful method for either
training new language-aware image encoders or augmenting existing pretrained models …
training new language-aware image encoders or augmenting existing pretrained models …
Fast and scalable search of whole-slide images via self-supervised deep learning
The adoption of digital pathology has enabled the curation of large repositories of gigapixel
whole-slide images (WSIs). Computationally identifying WSIs with similar morphologic …
whole-slide images (WSIs). Computationally identifying WSIs with similar morphologic …
A graph-transformer for whole slide image classification
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 …
performing supervised deep learning, a WSI is divided into small patches, trained and the …
Survival prediction across diverse cancer types using neural networks
X Yan, W Wang, M ** is a fundamental task in learning objective characterizations of
histopathologic biomarkers within the tumor-immune microenvironment in cancer pathology …
histopathologic biomarkers within the tumor-immune microenvironment in cancer pathology …
Gtp-4o: Modality-prompted heterogeneous graph learning for omni-modal biomedical representation
Recent advances in learning multi-modal representation have witnessed the success in
biomedical domains. While established techniques enable handling multi-modal …
biomedical domains. While established techniques enable handling multi-modal …