AIMC Topic: Image Processing, Computer-Assisted

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Deep Learning-Based Denoising in High-Speed Portable Reflectance Confocal Microscopy.

Lasers in surgery and medicine
BACKGROUND AND OBJECTIVE: Portable confocal microscopy (PCM) is a low-cost reflectance confocal microscopy technique that can visualize cellular details of human skin in vivo. When PCM images are acquired with a short exposure time to reduce motion b...

Transfer learning in medical image segmentation: New insights from analysis of the dynamics of model parameters and learned representations.

Artificial intelligence in medicine
We present a critical assessment of the role of transfer learning in training fully convolutional networks (FCNs) for medical image segmentation. We first show that although transfer learning reduces the training time on the target task, improvements...

Incorporation of residual attention modules into two neural networks for low-dose CT denoising.

Medical physics
PURPOSE: The low-dose computed tomography (CT) imaging can reduce the damage caused by x-ray radiation to the human body. However, low-dose CT images have a different degree of artifacts than conventional CT images, and their resolution is lower than...

Automatic glaucoma detection based on transfer induced attention network.

Biomedical engineering online
BACKGROUND: Glaucoma is one of the causes that leads to irreversible vision loss. Automatic glaucoma detection based on fundus images has been widely studied in recent years. However, existing methods mainly depend on a considerable amount of labeled...

Semantic segmentation of human oocyte images using deep neural networks.

Biomedical engineering online
BACKGROUND: Infertility is a significant problem of humanity. In vitro fertilisation is one of the most effective and frequently applied ART methods. The effectiveness IVF depends on the assessment and selection of gametes and embryo with the highest...

Ground-truth-free deep learning for artefacts reduction in 2D radial cardiac cine MRI using a synthetically generated dataset.

Physics in medicine and biology
In this work, we consider the task of image reconstruction in 2D radial cardiac cine MRI using deep learning (DL)-based regularization. As the regularization is achieved by employing an image-prior predicted by a pre-trained convolutional neural netw...

Generative Adversarial Networks: A Primer for Radiologists.

Radiographics : a review publication of the Radiological Society of North America, Inc
Artificial intelligence techniques involving the use of artificial neural networks-that is, deep learning techniques-are expected to have a major effect on radiology. Some of the most exciting applications of deep learning in radiology make use of ge...

Research on nondestructive identification of grape varieties based on EEMD-DWT and hyperspectral image.

Journal of food science
Grape varieties are directly related to the quality and sales price of table grapes and consumed products (raisin, wine, grape juice, etc.). To satisfy the identification requirements of rapid, accurate, and nondestructive detection, an improved deno...

Brain graph super-resolution using adversarial graph neural network with application to functional brain connectivity.

Medical image analysis
Brain image analysis has advanced substantially in recent years with the proliferation of neuroimaging datasets acquired at different resolutions. While research on brain image super-resolution has undergone a rapid development in the recent years, b...