AIMC Topic: Deep Learning

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Artificial intelligence in multiparametric prostate cancer imaging with focus on deep-learning methods.

Computer methods and programs in biomedicine
Prostate cancer represents today the most typical example of a pathology whose diagnosis requires multiparametric imaging, a strategy where multiple imaging techniques are combined to reach an acceptable diagnostic performance. However, the reviewing...

Gait characteristics and clinical relevance of hereditary spinocerebellar ataxia on deep learning.

Artificial intelligence in medicine
BACKGROUND: Deep learning has always been at the forefront of scientific research. It has also been applied to medical research. Hereditary spinocerebellar ataxia (SCA) is characterized by gait abnormalities and is usually evaluated semi-quantitative...

Bayesian deep matrix factorization network for multiple images denoising.

Neural networks : the official journal of the International Neural Network Society
This paper aims at proposing a robust and fast low rank matrix factorization model for multiple images denoising. To this end, a novel model, Bayesian deep matrix factorization network (BDMF), is presented, where a deep neural network (DNN) is design...

Multi-task learning for the prediction of wind power ramp events with deep neural networks.

Neural networks : the official journal of the International Neural Network Society
In Machine Learning, the most common way to address a given problem is to optimize an error measure by training a single model to solve the desired task. However, sometimes it is possible to exploit latent information from other related tasks to impr...

A Multi-Task Framework for Facial Attributes Classification through End-to-End Face Parsing and Deep Convolutional Neural Networks.

Sensors (Basel, Switzerland)
Human face image analysis is an active research area within computer vision. In this paper we propose a framework for face image analysis, addressing three challenging problems of race, age, and gender recognition through face parsing. We manually la...

Radon Inversion via Deep Learning.

IEEE transactions on medical imaging
The Radon transform is widely used in physical and life sciences, and one of its major applications is in medical X-ray computed tomography (CT), which is significantly important in disease screening and diagnosis. In this paper, we propose a novel r...

Deep Learning of Imaging Phenotype and Genotype for Predicting Overall Survival Time of Glioblastoma Patients.

IEEE transactions on medical imaging
Glioblastoma (GBM) is the most common and deadly malignant brain tumor. For personalized treatment, an accurate pre-operative prognosis for GBM patients is highly desired. Recently, many machine learning-based methods have been adopted to predict ove...

Deep Learning-Based Super-resolution Ultrasound Speckle Tracking Velocimetry.

Ultrasound in medicine & biology
Deep ultrasound localization microscopy (deep-ULM) allows sub-wavelength resolution imaging with deep learning. However, the injection of contrast agents (CAs) in deep-ULM is debatable because of their potential risk. In this study, we propose a deep...

Deep Learning to Generate Chemical Property Libraries and Candidate Molecules for Small Molecule Identification in Complex Samples.

Analytical chemistry
Comprehensive and unambiguous identification of small molecules in complex samples will revolutionize our understanding of the role of metabolites in biological systems. Existing and emerging technologies have enabled measurement of chemical properti...

Segmentation of organs-at-risk in cervical cancer CT images with a convolutional neural network.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: We introduced and evaluated an end-to-end organs-at-risk (OARs) segmentation model that can provide accurate and consistent OARs segmentation results in much less time.