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Found 6 papers, 1 papers with code
Mixed Supervision of Histopathology Improves Prostate Cancer Classification from MRI
To address this, we present an MRI-based deep learning method for predicting clinically significant prostate cancer applicable to a patient population with subsequent ground truth biopsy results ranging from benign pathology to ISUP grade group~5.
Physics-driven Deep Learning for PET/MRI
In this paper, we review physics- and data-driven reconstruction techniques for simultaneous positron emission tomography (PET) / magnetic resonance imaging (MRI) systems, which have significant advantages for clinical imaging of cancer, neurological disorders, and heart disease.
Synthetic PET via Domain Translation of 3D MRI
Historically, patient datasets have been used to develop and validate various reconstruction algorithms for PET/MRI and PET/CT.
Federated Learning with Research Prototypes for Multi-Center MRI-based Detection of Prostate Cancer with Diverse Histopathology
Early prostate cancer detection and staging from MRI are extremely challenging tasks for both radiologists and deep learning algorithms, but the potential to learn from large and diverse datasets remains a promising avenue to increase their generalization capability both within- and across clinics.
Predicting Generalization in Deep Learning via Local Measures of Distortion
We study generalization in deep learning by appealing to complexity measures originally developed in approximation and information theory.
Deep Algorithms: designs for networks
A new design methodology for neural networks that is guided by traditional algorithm design is presented.
