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Found 5 papers, 1 papers with code
Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applications
The real-world implementation of federated learning is complex and requires research and development actions at the crossroad between different domains ranging from data science, to software programming, networking, and security.
Prediction of brain tumor recurrence location based on multi-modal fusion and nonlinear correlation learning
In this paper, we present a deep learning-based brain tumor recurrence location prediction network.
Multi-Task Multi-Scale Learning For Outcome Prediction in 3D PET Images
Conclusions: We show that, by using a multi-task learning approach, we can boost the performance of radiomic analysis by extracting rich information of intratumoral and peritumoral regions.
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.
Weakly Supervised PET Tumor Detection Using Class Response
In this paper, we present a novel approach to locate different type of lesions in positron emission tomography (PET) images using only a class label at the image-level.
