This research investigates the trustworthiness and fairness of nonparametric deep survival models for analyzing Alzheimer's Disease (AD) progression. It addresses the lack of studies considering learned bias in existing deep learning models for AD and proposes novel fairness metrics to ensure reliable predictions.
This paper addresses challenges in Whole Slide Images (WSIs) survival analysis, specifically the computational bottleneck of Transformers and Mamba's sensitivity to input order and unidirectional architecture. It proposes a novel approach to overcome Mamba's limitations in capturing topological connectivity and bidirectional spatial structures.
This research explores the application of deep neural networks in survival analysis, employing a multi-task framework. The approach aims to enhance the prediction and modeling of time-to-event data by leveraging complex neural network architectures.
This paper proposes non-parametric estimators, KM-ARL and KM-ADD, for the average run length and average detection delay in quickest changepoint detection. These estimators are designed for finite and irregular sequence lengths, enhancing robustness and interpretability in real-world datasets.