AIMC Topic: Image Processing, Computer-Assisted

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U-Net-Based Medical Image Segmentation.

Journal of healthcare engineering
Deep learning has been extensively applied to segmentation in medical imaging. U-Net proposed in 2015 shows the advantages of accurate segmentation of small targets and its scalable network architecture. With the increasing requirements for the perfo...

Benchmarking of deep learning algorithms for 3D instance segmentation of confocal image datasets.

PLoS computational biology
Segmenting three-dimensional (3D) microscopy images is essential for understanding phenomena like morphogenesis, cell division, cellular growth, and genetic expression patterns. Recently, deep learning (DL) pipelines have been developed, which claim ...

Deep learning -- promises for 3D nuclear imaging: a guide for biologists.

Journal of cell science
For the past century, the nucleus has been the focus of extensive investigations in cell biology. However, many questions remain about how its shape and size are regulated during development, in different tissues, or during disease and aging. To trac...

State-of-the-art retinal vessel segmentation with minimalistic models.

Scientific reports
The segmentation of retinal vasculature from eye fundus images is a fundamental task in retinal image analysis. Over recent years, increasingly complex approaches based on sophisticated Convolutional Neural Network architectures have been pushing per...

A generative adversarial network for synthetization of regions of interest based on digital mammograms.

Scientific reports
Deep learning (DL) models are becoming pervasive and applicable to computer vision, image processing, and synthesis problems. The performance of these models is often improved through architectural configuration, tweaks, the use of enormous training ...

Using deep learning to predict abdominal age from liver and pancreas magnetic resonance images.

Nature communications
With age, the prevalence of diseases such as fatty liver disease, cirrhosis, and type two diabetes increases. Approaches to both predict abdominal age and identify risk factors for accelerated abdominal age may ultimately lead to advances that will d...

Detection of Diseases Using Machine Learning Image Recognition Technology in Artificial Intelligence.

Computational intelligence and neuroscience
With the continuous development and improvement of artificial intelligence technology, machine learning technology has also been extensively developed, which has promoted the development of computer vision, image processing, natural language processi...

Pseudo low-energy monochromatic imaging of head and neck cancers: Deep learning image reconstruction with dual-energy CT.

International journal of computer assisted radiology and surgery
PURPOSE: Low-energy virtual monochromatic images (VMIs) derived from dual-energy computed tomography (DECT) systems improve lesion conspicuity of head and neck cancer over single-energy CT (SECT). However, DECT systems are installed in a limited numb...

Medical image segmentation model based on triple gate MultiLayer perceptron.

Scientific reports
To alleviate the social contradiction between limited medical resources and increasing medical needs, the medical image-assisted diagnosis based on deep learning has become the research focus in Wise Information Technology of med. Most of the existin...

Simple, fast, and flexible framework for matrix completion with infinite width neural networks.

Proceedings of the National Academy of Sciences of the United States of America
Matrix completion problems arise in many applications including recommendation systems, computer vision, and genomics. Increasingly larger neural networks have been successful in many of these applications but at considerable computational costs. Rem...