The emergence of deep learning in the medical field has popularized the development of
models to predict survival outcomes from histopathology images in precision oncology …

Survival prediction is a complex ordinal regression task that aims to predict the survival
coefficient ranking among a cohort of patients, typically achieved by analyzing patients' …

Patient survival prediction based on gigapixel whole-slide histopathological images (WSIs)
has become increasingly prevalent in recent years. A key challenge of this task is achieving …

Survival prediction for patients based on histopa-thological whole-slide images (WSIs) has
attracted increasing attention in recent years. Due to the massive pixel data in a single WSI …

Cancer survival prediction requires considering not only the biological morphology but also
the contextual interactions of tumor and surrounding tissues. The major limitation of previous …

Histopathology image-based survival prediction aims to provide a precise assessment of
cancer prognosis and can inform personalized treatment decision-making in order to …

Survival analysis from whole slide histopathology images (WSIs) is challenging due to the
high dimensionality of WSIs. Graph-based approaches thus have been extensively studied …

Understanding prognosis and mortality is critical for evaluating the treatment plan of
patients. Advances in digital pathology and deep learning techniques have made it practical …

Cancer diagnosis, prognosis, and therapeutic response prediction are heavily influenced by
the relationship between the histopathological structures and the function of the tissue …

With the remarkable success of representation learning for prediction problems, we have
witnessed a rapid expansion of the use of machine learning and deep learning for the …