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Found 6 papers, 2 papers with code
Deep evidential fusion with uncertainty quantification and contextual discounting for multimodal medical image segmentation
Single-modality medical images generally do not contain enough information to reach an accurate and reliable diagnosis.
Lymphoma segmentation from 3D PET-CT images using a deep evidential network
The architecture is composed of a deep feature-extraction module and an evidential layer.
AI-Based Detection, Classification and Prediction/Prognosis in Medical Imaging: Towards Radiophenomics
Artificial intelligence (AI) techniques have significant potential to enable effective, robust and automated image phenotyping including identification of subtle patterns.
Deep PET/CT fusion with Dempster-Shafer theory for lymphoma segmentation
Single-modality volumes are trained separately to get initial segmentation maps and an evidential fusion layer is proposed to fuse the two pieces of evidence using Dempster-Shafer theory (DST).
Evidential segmentation of 3D PET/CT images
In this paper, a segmentation method based on belief functions is proposed to segment lymphomas in 3D PET/CT images.
RADIOGAN: Deep Convolutional Conditional Generative adversarial Network To Generate PET Images
Using generative adversarial networks (GAN) is a promising way to address this problem, however, it is challenging to train one model to generate different classes of lesions.
