In recent years, the diagnosis of gliomas has become increasingly complex. Analysis of
glioma histopathology images using artificial intelligence (AI) offers new opportunities to …

Survival outcome assessment is challenging and inherently associated with multiple clinical
factors (eg, imaging and genomics biomarkers) in cancer. Enabling multimodal analytics …

Modern deep learning techniques on automatic multimodal medical diagnosis rely on
massive expert annotations, which is time-consuming and prohibitive. Recent masked …

Multimodal medical imaging plays a pivotal role in clinical diagnosis and research, as it
combines information from various imaging modalities to provide a more comprehensive …

Most recently, the pathology diagnosis of cancer is shifting to integrating molecular makers
with histology features. It is a urgent need for digital pathology methods to effectively …

Cortical cataract, a common type of cataract, is particularly difficult to be diagnosed
automatically due to the complex features of the lesions. Recently, many methods based on …

Disease assessment involves two stages of decision-making process. The first process is to
infer the development trend of the disease through the existing judgment conditions, which …

The fusion of multi-modal data, eg, pathology slides and genomic profiles, can provide
complementary information and benefit glioma grading. However, genomic profiles are …

Multimodal learning with incomplete input data (missing modality) is very practical and
challenging. In this work, we conduct an in-depth analysis of this challenge and find that …

Abstract Machine learning (ML) applications in medical artificial intelligence (AI) systems
have shifted from traditional and statistical methods to increasing application of deep …