[HTML][HTML] Artificial intelligence for multimodal data integration in oncology
In oncology, the patient state is characterized by a whole spectrum of modalities, ranging
from radiology, histology, and genomics to electronic health records. Current artificial …
from radiology, histology, and genomics to electronic health records. Current artificial …
The 2022 World Health Organization classification of tumors of the urinary system and male genital organs—part B: prostate and urinary tract tumors
Abstract The 2022 World Health Organization (WHO) classification of the urinary and male
genital tumors was recently published by the International Agency for Research on Cancer …
genital tumors was recently published by the International Agency for Research on Cancer …
Towards a general-purpose foundation model for computational pathology
Quantitative evaluation of tissue images is crucial for computational pathology (CPath) tasks,
requiring the objective characterization of histopathological entities from whole-slide images …
requiring the objective characterization of histopathological entities from whole-slide images …
[HTML][HTML] Deep learning-enabled virtual histological staining of biological samples
Histological staining is the gold standard for tissue examination in clinical pathology and life-
science research, which visualizes the tissue and cellular structures using chromatic dyes or …
science research, which visualizes the tissue and cellular structures using chromatic dyes or …
A visual-language foundation model for computational pathology
The accelerated adoption of digital pathology and advances in deep learning have enabled
the development of robust models for various pathology tasks across a diverse array of …
the development of robust models for various pathology tasks across a diverse array of …
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 …
Benchmarking self-supervised learning on diverse pathology datasets
Computational pathology can lead to saving human lives, but models are annotation hungry
and pathology images are notoriously expensive to annotate. Self-supervised learning has …
and pathology images are notoriously expensive to annotate. Self-supervised learning has …
The multimodality cell segmentation challenge: toward universal solutions
Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images.
Existing cell segmentation methods are often tailored to specific modalities or require …
Existing cell segmentation methods are often tailored to specific modalities or require …
[HTML][HTML] Demystifying supervised learning in healthcare 4.0: A new reality of transforming diagnostic medicine
The global healthcare sector continues to grow rapidly and is reflected as one of the fastest-
growing sectors in the fourth industrial revolution (4.0). The majority of the healthcare …
growing sectors in the fourth industrial revolution (4.0). The majority of the healthcare …
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 …