Computational histopathology is focused on the automatic analysis of rich phenotypic
information contained in gigabyte whole slide images, aiming at providing cancer patients …

Transformers have been widely used in many computer vision challenges and have shown
the capability of producing better results than convolutional neural networks (CNNs). Taking …

Identifying the relationship between imaging features and genetic variation (a term coined
“imaging genomics”) offers valuable insight into the pathogenesis of cancer, as well as …

Pathological images and molecular omics are important information for predicting diagnosis
and prognosis. The two kinds of heterogeneous modal data contain complementary …

Histopathological whole slide images (WSIs) are the gold standard for cancer diagnosis. In
prognosis, WSIs can also help predict the overall survival (OS) time of cancer (such as …

Gastrointestinal cancer is becoming increasingly common, which leads to over 3 million
deaths every year. No typical symptoms appear in the early stage of gastrointestinal cancer …

Colorectal cancer is the third most prevalent cancer worldwide, and its treatment has been a
demanding clinical problem. Beyond traditional surgical therapy and chemotherapy, newly …

We study the problem of estimating survival causal effects, where the aim is to characterize
the impact of an intervention on survival times, ie, how long it takes for an event to occur …

With the tremendous progress brought by artificial intelligence, many whole-slide
pathological images (WSIs) based machine learning models are designed to estimate the …

Hematoxylin and eosin (H&E) whole slide images provide valuable information for
predicting prognostic outcomes in colorectal cancer (CRC) patients. However, extracting …