AIMC Topic: Deep Learning

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Technical and clinical overview of deep learning in radiology.

Japanese journal of radiology
Deep learning has been applied to clinical applications in not only radiology, but also all other areas of medicine. This review provides a technical and clinical overview of deep learning in radiology. To gain a more practical understanding of deep ...

Transfer learning improves resting-state functional connectivity pattern analysis using convolutional neural networks.

GigaScience
BACKGROUND: Deep learning is gaining importance in the prediction of cognitive states and brain pathology based on neuroimaging data. Including multiple hidden layers in artificial neural networks enables unprecedented predictive power; however, the ...

Nasal cytology with deep learning techniques.

International journal of medical informatics
BACKGROUND: In recent years, cytological observations in the Rhinology field are being increasingly utilized. This development has taken place over the last two decades and has proven to be fundamental in defining new nosological entities and in driv...

Tox_(R)CNN: Deep learning-based nuclei profiling tool for drug toxicity screening.

PLoS computational biology
Toxicity is an important factor in failed drug development, and its efficient identification and prediction is a major challenge in drug discovery. We have explored the potential of microscopy images of fluorescently labeled nuclei for the prediction...

3D Auto-Context-Based Locality Adaptive Multi-Modality GANs for PET Synthesis.

IEEE transactions on medical imaging
Positron emission tomography (PET) has been substantially used recently. To minimize the potential health risk caused by the tracer radiation inherent to PET scans, it is of great interest to synthesize the high-quality PET image from the low-dose on...

QuickNAT: A fully convolutional network for quick and accurate segmentation of neuroanatomy.

NeuroImage
Whole brain segmentation from structural magnetic resonance imaging (MRI) is a prerequisite for most morphological analyses, but is computationally intense and can therefore delay the availability of image markers after scan acquisition. We introduce...

Improvement of image quality at CT and MRI using deep learning.

Japanese journal of radiology
Deep learning has been developed by computer scientists. Here, we discuss techniques for improving the image quality of diagnostic computed tomography and magnetic resonance imaging with the aid of deep learning. We categorize the techniques for impr...

Applying deep learning to right whale photo identification.

Conservation biology : the journal of the Society for Conservation Biology
Photo identification is an important tool for estimating abundance and monitoring population trends over time. However, manually matching photographs to known individuals is time-consuming. Motivated by recent developments in image recognition, we ho...