As a promising area in machine learning, multi-task learning (MTL) aims to improve the
performance of multiple related learning tasks by leveraging useful information among them …

Survival analysis is a subfield of statistics where the goal is to analyze and model data
where the outcome is the time until an event of interest occurs. One of the main challenges …

Traditional image-based survival prediction models rely on discriminative patch labeling
which make those methods not scalable to extend to large datasets. Recent studies have …

Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to
leverage useful information contained in multiple related tasks to help improve the …

Background Prediction models for time-to-event outcomes are commonly used in biomedical
research to obtain subject-specific probabilities that aid in making important clinical care …

In this paper, we study a novel knowledge transfer task in the domain of graph neural
networks (GNNs). We strive to train a multi-talented student GNN, without accessing human …

Image-based precision medicine techniques can be used to better treat cancer patients.
However, the gigapixel resolution of Whole Slide Histopathological Images (WSIs) makes …

Traditional Cox proportional hazard model for survival analysis are based on structured
features like patients' sex, smoke years, BMI, etc. With the development of medical imaging …

Deep neural networks have been used in survival prediction by providing high-quality
features. However, few works have noticed the significant role of topological features of …

We describe a new approach to estimating relative risks in time-to-event prediction problems
with censored data in a fully parametric manner. Our approach does not require making …