AIMC Topic: Image Processing, Computer-Assisted

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Uncorrelated feature encoding for faster image style transfer.

Neural networks : the official journal of the International Neural Network Society
Recent image style transfer methods use a pre-trained convolutional neural network as their feature encoder. However, the pre-trained network is not optimal for image style transfer but rather for image classification. Furthermore, they require time-...

Deep Neural Networks for Image-Based Dietary Assessment.

Journal of visualized experiments : JoVE
Due to the issues and costs associated with manual dietary assessment approaches, automated solutions are required to ease and speed up the work and increase its quality. Today, automated solutions are able to record a person's dietary intake in a mu...

Machine learning in Magnetic Resonance Imaging: Image reconstruction.

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)
Magnetic Resonance Imaging (MRI) plays a vital role in diagnosis, management and monitoring of many diseases. However, it is an inherently slow imaging technique. Over the last 20 years, parallel imaging, temporal encoding and compressed sensing have...

Decoding with confidence: Statistical control on decoder maps.

NeuroImage
In brain imaging, decoding is widely used to infer relationships between brain and cognition, or to craft brain-imaging biomarkers of pathologies. Yet, standard decoding procedures do not come with statistical guarantees, and thus do not give confide...

Do Radiographic Assessments of Periodontal Bone Loss Improve with Deep Learning Methods for Enhanced Image Resolution?

Sensors (Basel, Switzerland)
Resolution plays an essential role in oral imaging for periodontal disease assessment. Nevertheless, due to limitations in acquisition tools, a considerable number of oral examinations have low resolution, making the evaluation of this kind of lesion...

Quantitative PET in the 2020s: a roadmap.

Physics in medicine and biology
Positron emission tomography (PET) plays an increasingly important role in research and clinical applications, catalysed by remarkable technical advances and a growing appreciation of the need for reliable, sensitive biomarkers of human function in h...

Machine Learning Generated Synthetic Faces for Use in Facial Aesthetic Research.

Facial plastic surgery & aesthetic medicine
A centralized repository of clinically applicable facial images with unrestricted use would facilitate facial aesthetic research. Using a machine learning neural network, we aim to (1) create a repository of synthetic faces that can be used for fac...

Deep neural network ensemble for on-the-fly quality control-driven segmentation of cardiac MRI T1 mapping.

Medical image analysis
Recent developments in artificial intelligence have generated increasing interest to deploy automated image analysis for diagnostic imaging and large-scale clinical applications. However, inaccuracy from automated methods could lead to incorrect conc...

Rapid analysis of streaming platelet images by semi-unsupervised learning.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
We developed a fast and accurate deep learning approach employing a semi-unsupervised learning system (SULS) for capturing the real-time noisy, sparse, and ambiguous images of platelet activation. Outperforming several leading supervised learning met...

A Systematic Approach for MRI Brain Tumor Localization and Segmentation Using Deep Learning and Active Contouring.

Journal of healthcare engineering
One of the main requirements of tumor extraction is the annotation and segmentation of tumor boundaries correctly. For this purpose, we present a threefold deep learning architecture. First, classifiers are implemented with a deep convolutional neura...