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Found 8 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.
A Multi-view Impartial Decision Network for Frontotemporal Dementia Diagnosis
To achieve this, we employ multiple expert models to extract evidence from the abundant neural network information contained in fMRI images.
SAM-U: Multi-box prompts triggered uncertainty estimation for reliable SAM in medical image
Recently, Segmenting Anything has taken an important step towards general artificial intelligence.
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.
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.
