AIMC Topic: Image Processing, Computer-Assisted

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Effective deep learning approaches for predicting COVID-19 outcomes from chest computed tomography volumes.

Scientific reports
The rapid evolution of the novel coronavirus disease (COVID-19) pandemic has resulted in an urgent need for effective clinical tools to reduce transmission and manage severe illness. Numerous teams are quickly developing artificial intelligence appro...

SMU-Net: Saliency-Guided Morphology-Aware U-Net for Breast Lesion Segmentation in Ultrasound Image.

IEEE transactions on medical imaging
Deep learning methods, especially convolutional neural networks, have been successfully applied to lesion segmentation in breast ultrasound (BUS) images. However, pattern complexity and intensity similarity between the surrounding tissues (i.e., back...

Propagating Uncertainty Across Cascaded Medical Imaging Tasks for Improved Deep Learning Inference.

IEEE transactions on medical imaging
Although deep networks have been shown to perform very well on a variety of medical imaging tasks, inference in the presence of pathology presents several challenges to common models. These challenges impede the integration of deep learning models in...

Genetic U-Net: Automatically Designed Deep Networks for Retinal Vessel Segmentation Using a Genetic Algorithm.

IEEE transactions on medical imaging
Recently, many methods based on hand-designed convolutional neural networks (CNNs) have achieved promising results in automatic retinal vessel segmentation. However, these CNNs remain constrained in capturing retinal vessels in complex fundus images....

A low-cost, long-term underwater camera trap network coupled with deep residual learning image analysis.

PloS one
Understanding long-term trends in marine ecosystems requires accurate and repeatable counts of fishes and other aquatic organisms on spatial and temporal scales that are difficult or impossible to achieve with diver-based surveys. Long-term, spatiall...

First Gradually, Then Suddenly: Understanding the Impact of Image Compression on Object Detection Using Deep Learning.

Sensors (Basel, Switzerland)
Video surveillance systems process high volumes of image data. To enable long-term retention of recorded images and because of the data transfer limitations in geographically distributed systems, lossy compression is commonly applied to images prior ...

Automatic mapping of multiplexed social receptive fields by deep learning and GPU-accelerated 3D videography.

Nature communications
Social interactions powerfully impact the brain and the body, but high-resolution descriptions of these important physical interactions and their neural correlates are lacking. Currently, most studies rely on labor-intensive methods such as manual an...

Ultrafast water-fat separation using deep learning-based single-shot MRI.

Magnetic resonance in medicine
PURPOSE: To present a deep learning-based reconstruction method for spatiotemporally encoded single-shot MRI to simultaneously obtain water and fat images.

Application of deep learning image reconstruction in low-dose chest CT scan.

The British journal of radiology
OBJECTIVE: Deep learning image reconstruction (DLIR) is a new reconstruction method for maintaining image quality at reduced radiation dose. The purpose of this study was to compare image quality of reduced-dose DLIR images with the standard-dose ada...

Automated procedure to assess pup retrieval in laboratory mice.

Scientific reports
All mammalian mothers form some sort of caring bond with their infants that is crucial to the development of their offspring. The Pup Retrieval Test (PRT) is the leading procedure to assess pup-directed maternal care in laboratory rodents, used in a ...