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 …

Survival prediction using whole slide images (WSIs) can provide guidance for better
treatment of diseases and patient care. Previous methods usually extract and process only …

Recent deep learning techniques have shown promising performance on survival prediction
from Whole Slide Images (WSIs). These methods are often based on multiple-step …

Understanding of prognosis and mortality is crucial for evaluating the treatment plans for
patients. Recent developments of digital pathology and deep learning bring the possibility of …

Histopathological Whole Slide Images (WSIs) at giga-pixel resolution are the gold standard
for cancer analysis and prognosis. Due to the scarcity of pixel-or patch-level annotations of …

The use of multi-modal data such as the combination of whole slide images (WSIs) and
gene expression data for survival analysis can lead to more accurate survival predictions …

The Transformer-based methods provide a good opportunity for modeling the global context
of gigapixel whole slide image (WSI), however, there are still two main problems in applying …

Recent advancements in computer vision and neural networks have facilitated the medical
imaging survival analysis for various medical applications. However, challenges arise when …

Deep-learning-based survival prediction can assist doctors by providing additional
information for diagnosis by estimating the risk or time of death. The former focuses on …

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 …