AIMC Topic: Image Processing, Computer-Assisted

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Learning-based estimation of dielectric properties and tissue density in head models for personalized radio-frequency dosimetry.

Physics in medicine and biology
Radio-frequency dosimetry is an important process in assessments for human exposure safety and for compliance of related products. Recently, computational human models generated from medical images have often been used for such assessment, especially...

A deep-learning-based approach for adenoid hypertrophy diagnosis.

Medical physics
PURPOSE: Adenoid hypertrophy is a pathological hyperplasia of adenoids and may cause snoring, apnea, and impede breathing during sleep. In clinical practice, radiologists diagnose the severity of adenoid hypertrophy by measuring the ratio of adenoid ...

Supervised and unsupervised language modelling in Chest X-Ray radiological reports.

PloS one
Chest radiography (CXR) is the most commonly used imaging modality and deep neural network (DNN) algorithms have shown promise in effective triage of normal and abnormal radiograms. Typically, DNNs require large quantities of expertly labelled traini...

CAPTCHA Image Generation: Two-Step Style-Transfer Learning in Deep Neural Networks.

Sensors (Basel, Switzerland)
Mobile devices such as sensors are used to connect to the Internet and provide services to users. Web services are vulnerable to automated attacks, which can restrict mobile devices from accessing websites. To prevent such automated attacks, CAPTCHAs...

Accelerated Acquisition of High-resolution Diffusion-weighted Imaging of the Brain with a Multi-shot Echo-planar Sequence: Deep-learning-based Denoising.

Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine
To accelerate high-resolution diffusion-weighted imaging with a multi-shot echo-planar sequence, we propose an approach based on reduced averaging and deep learning. Denoising convolutional neural networks can reduce amplified noise without requiring...

Hyperspectral imaging for accurate determination of rice variety using a deep learning network with multi-feature fusion.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
The phenomena of rice adulteration and shoddy rice arise continuously in high-quality rice and reduce the interests of producers, consumers and traders. Hyperspectral imaging (HSI) was conducted to determine rice variety using a deep learning network...

Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer.

Nature communications
Accurate identification of axillary lymph node (ALN) involvement in patients with early-stage breast cancer is important for determining appropriate axillary treatment options and therefore avoiding unnecessary axillary surgery and complications. Her...

A supervised blood vessel segmentation technique for digital Fundus images using Zernike Moment based features.

PloS one
This paper proposes a new supervised method for blood vessel segmentation using Zernike moment-based shape descriptors. The method implements a pixel wise classification by computing a 11-D feature vector comprising of both statistical (gray-level) f...

Adaptive and Compressive Beamforming Using Deep Learning for Medical Ultrasound.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
In ultrasound (US) imaging, various types of adaptive beamforming techniques have been investigated to improve the resolution and the contrast-to-noise ratio of the delay and sum (DAS) beamformers. Unfortunately, the performance of these adaptive bea...

Artificial intelligence and convolution neural networks assessing mammographic images: a narrative literature review.

Journal of medical radiation sciences
Studies have shown that the use of artificial intelligence can reduce errors in medical image assessment. The diagnosis of breast cancer is an essential task; however, diagnosis can include 'detection' and 'interpretation' errors. Studies to reduce t...