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Found 8 papers, 2 papers with code
Hierarchical Transformer for Survival Prediction Using Multimodality Whole Slide Images and Genomics
Learning good representation of giga-pixel level whole slide pathology images (WSI) for downstream tasks is critical.
Hierarchical Proxy-based Loss for Deep Metric Learning
In this paper, we present a framework that leverages this implicit hierarchy by imposing a hierarchical structure on the proxies and can be used with any existing proxy-based loss.
Whole Slide Images based Cancer Survival Prediction using Attention Guided Deep Multiple Instance Learning Networks
We evaluated our methods on two large cancer whole slide images datasets and our results suggest that the proposed approach is more effective and suitable for large datasets and has better interpretability in locating important patterns and features that contribute to accurate cancer survival predictions.
Label-Driven Reconstruction for Domain Adaptation in Semantic Segmentation
Unsupervised domain adaptation enables to alleviate the need for pixel-wise annotation in the semantic segmentation.
Ranked #26 on
Domain Adaptation
on SYNTHIA-to-Cityscapes
Robust Contextual Bandit via the Capped-$\ell_{2}$ norm
In the critic updating, the capped-$\ell_{2}$ norm is used to measure the approximation error, which prevents outliers from dominating our objective.
WSISA: Making Survival Prediction From Whole Slide Histopathological Images
Different from existing state-of-the-arts image-based survival models which extract features using some patches from small regions of WSIs, the proposed framework can efficiently exploit and utilize all discriminative patterns in WSIs to predict patients' survival status.
Cohesion-based Online Actor-Critic Reinforcement Learning for mHealth Intervention
In this paper, we propose a network cohesion constrained (actor-critic) Reinforcement Learning (RL) method for mHealth.
10,000+ Times Accelerated Robust Subset Selection (ARSS)
Subset selection from massive data with noised information is increasingly popular for various applications.
