Search Results for author:
Found 11 papers, 4 papers with code
MedRG: Medical Report Grounding with Multi-modal Large Language Model
Medical Report Grounding is pivotal in identifying the most relevant regions in medical images based on a given phrase query, a critical aspect in medical image analysis and radiological diagnosis.
Destination-Constrained Linear Dynamical System Modeling in Set-Valued Frameworks
Directional motion towards a specified destination is a common occurrence in physical processes and human societal activities.
Uncertainty-informed Mutual Learning for Joint Medical Image Classification and Segmentation
Our UML has the potential to explore the development of more reliable and explainable medical image analysis models.
Reliable Multimodality Eye Disease Screening via Mixture of Student's t Distributions
To address this issue, we introduce a novel multimodality evidential fusion pipeline for eye disease screening, EyeMoSt, which provides a measure of confidence for unimodality and elegantly integrates the multimodality information from a multi-distribution fusion perspective.
A Review of Uncertainty Estimation and its Application in Medical Imaging
We further discuss how they can be estimated in medical imaging.
Towards Reliable Medical Image Segmentation by utilizing Evidential Calibrated Uncertainty
DEviS not only enhances the calibration and robustness of baseline segmentation accuracy but also provides high-efficiency uncertainty estimation for reliable predictions.
Seamless Tracking of Group Targets and Ungrouped Targets Using Belief Propagation
This paper considers the problem of tracking a large-scale number of group targets.
TBraTS: Trusted Brain Tumor Segmentation
In our method, uncertainty is modeled explicitly using subjective logic theory, which treats the predictions of backbone neural network as subjective opinions by parameterizing the class probabilities of the segmentation as a Dirichlet distribution.
An Unbiased Symmetric Matrix Estimator for Topology Inference under Partial Observability
Network topology inference is a fundamental problem in many applications of network science, such as locating the source of fake news, brain connectivity networks detection, etc.
Towards Understanding the Impact of Model Size on Differential Private Classification
Then we propose a feature selection method to reduce the size of the model, based on a new metric which trades off the classification accuracy and privacy preserving.
Larger Model Causes Lower Classification Accuracy Under Differential Privacy: Reason and Solution
Differential privacy (DP) is an essential technique for privacy-preserving, which works by adding random noise to the data.
