The purpose of this research was to implement a deep learning network to overcome two of the major bottlenecks in improved image reconstruction for clinical positron emission tomography (PET). These are the lack of an automated means for the optimiza...
BACKGROUND: The development of standardised methods for ultrasound (US) scanning and evaluation of synovitis activity by the OMERACT-EULAR Synovitis Scoring (OESS) system is a major step forward in the use of US in the diagnosis and monitoring of pat...
Quantitative susceptibility mapping (QSM) is based on magnetic resonance imaging (MRI) phase measurements and has gained broad interest because it yields relevant information on biological tissue properties, predominantly myelin, iron and calcium in ...
An ENsemble Deep Learning Optimal Treatment (EndLot) approach is proposed for personalized medicine problems. The statistical framework of the proposed method is based on the outcome weighted learning (OWL) framework which transforms the optimal deci...
Early detection of cervical lesion is of great significance in reducing mortality from cervical cancer, and segmentation of cervical cell nuclei plays an important role in screening for cervical lesion. Compared with traditional algorithms, several d...
Neural networks : the official journal of the International Neural Network Society
Mar 28, 2019
This paper introduces novel deep architectures using the hybrid neural-kernel core model as the first building block. The proposed models follow a combination of a neural networks based architecture and a kernel based model enriched with pooling laye...
Convolutional Neural Networks (CNNs) require a large amount of annotated data to learn from, which is often difficult to obtain for medical imaging problems. In this work we show that the sample complexity of CNNs can be significantly improved by usi...
To enable the application of deep learning in biology, we present Selene (https://selene.flatironinstitute.org/), a PyTorch-based deep learning library for fast and easy development, training, and application of deep learning model architectures for ...
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