Medical vision-language models (VLMs) combine computer vision (CV) and natural
language processing (NLP) to analyze visual and textual medical data. Our paper reviews …

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

The advancements in data acquisition, storage, and processing techniques have resulted in
the rapid growth of heterogeneous medical data. Integrating radiological scans …

Artificial intelligence (AI) is revolutionizing head and neck cancer (HNC) care by providing
innovative tools that enhance diagnostic accuracy and personalize treatment strategies. This …

Continual learning (CL) is an approach to address catastrophic forgetting, which refers to
forgetting previously learned knowledge by neural networks when trained on new tasks or …

Relying solely on a single medical data for cancer diagnosis may increase the risk of
misdiagnosis and missed diagnosis. Multi-modal data provides comprehensive information …

Developing accurate machine learning models for oncology requires large-scale, high-
quality multimodal datasets. However, creating such datasets remains challenging due to …

Multi-omics research has enhanced our understanding of cancer heterogeneity and
progression. Investigating molecular data through multi-omics approaches is crucial for …

Alongside the continuous process of improving AI performance through the development of
more sophisticated models, researchers have also focused their attention to the emerging …

Simple Summary Pancreatic Ductal Adenocarcinoma (PDAC) remains one of the deadliest
forms of cancer, characterized by high rates of metastasis, late detection, and poor …