Search Results for author:
Found 6 papers, 4 papers with code
Gradient-Map-Guided Adaptive Domain Generalization for Cross Modality MRI Segmentation
Cross-modal MRI segmentation is of great value for computer-aided medical diagnosis, enabling flexible data acquisition and model generalization.
The Robust Semantic Segmentation UNCV2023 Challenge Results
This paper outlines the winning solutions employed in addressing the MUAD uncertainty quantification challenge held at ICCV 2023.
MILD: Modeling the Instance Learning Dynamics for Learning with Noisy Labels
Despite deep learning has achieved great success, it often relies on a large amount of training data with accurate labels, which are expensive and time-consuming to collect.
Modeling Multimodal Aleatoric Uncertainty in Segmentation with Mixture of Stochastic Experts
In this work, we focus on capturing the data-inherent uncertainty (aka aleatoric uncertainty) in segmentation, typically when ambiguities exist in input images.
Mutual Information-guided Knowledge Transfer for Novel Class Discovery
Our insight is to utilize mutual information to measure the relation between seen classes and unseen classes in a restricted label space and maximizing mutual information promotes transferring semantic knowledge.
Superpixel-guided Iterative Learning from Noisy Labels for Medical Image Segmentation
Learning segmentation from noisy labels is an important task for medical image analysis due to the difficulty in acquiring highquality annotations.
