AIMC Topic: Image Processing, Computer-Assisted

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KDE-GAN: A multimodal medical image-fusion model based on knowledge distillation and explainable AI modules.

Computers in biology and medicine
BACKGROUND: As medical images contain sensitive patient information, finding a publicly accessible dataset with patient permission is challenging. Furthermore, few large-scale datasets suitable for training image-fusion models are available. To addre...

Video Super-Resolution Method Using Deformable Convolution-Based Alignment Network.

Sensors (Basel, Switzerland)
With the advancement of sensors, image and video processing have developed for use in the visual sensing area. Among them, video super-resolution (VSR) aims to reconstruct high-resolution sequences from low-resolution sequences. To use consecutive co...

Feature Pyramid U-Net with Attention for Semantic Segmentation of Forward-Looking Sonar Images.

Sensors (Basel, Switzerland)
Forward-looking sonar is a technique widely used for underwater detection. However, most sonar images have underwater noise and low resolution due to their acoustic properties. In recent years, the semantic segmentation model U-Net has shown excellen...

MC-UNet: Multimodule Concatenation Based on U-Shape Network for Retinal Blood Vessels Segmentation.

Computational intelligence and neuroscience
Accurate retinal blood vessels segmentation is an important step in the clinical diagnosis of ophthalmic diseases. Many deep learning frameworks have come up for retinal blood vessels segmentation tasks. However, the complex vascular structure and un...

Deep Learning for Image Enhancement and Correction in Magnetic Resonance Imaging-State-of-the-Art and Challenges.

Journal of digital imaging
Magnetic resonance imaging (MRI) provides excellent soft-tissue contrast for clinical diagnoses and research which underpin many recent breakthroughs in medicine and biology. The post-processing of reconstructed MR images is often automated for incor...

Artificial Intelligence-Driven Ultra-Fast Superresolution MRI: 10-Fold Accelerated Musculoskeletal Turbo Spin Echo MRI Within Reach.

Investigative radiology
Magnetic resonance imaging (MRI) is the keystone of modern musculoskeletal imaging; however, long pulse sequence acquisition times may restrict patient tolerability and access. Advances in MRI scanners, coil technology, and innovative pulse sequence ...

A Review on Data Fusion of Multidimensional Medical and Biomedical Data.

Molecules (Basel, Switzerland)
Data fusion aims to provide a more accurate description of a sample than any one source of data alone. At the same time, data fusion minimizes the uncertainty of the results by combining data from multiple sources. Both aim to improve the characteriz...

Deep learning empowered volume delineation of whole-body organs-at-risk for accelerated radiotherapy.

Nature communications
In radiotherapy for cancer patients, an indispensable process is to delineate organs-at-risk (OARs) and tumors. However, it is the most time-consuming step as manual delineation is always required from radiation oncologists. Herein, we propose a ligh...

Explainable Deep-Learning-Assisted Sweat Assessment via a Programmable Colorimetric Chip.

Analytical chemistry
Multianalytes and individual differences of biofluids (such as blood, urine, or sweat) pose enormous complexity and challenges to rapid, facile, high-throughput, and accurate clinical analysis or health assessment. Deep-learning (DL)-assisted image a...

Deep-learning-based automatic facial bone segmentation using a two-dimensional U-Net.

International journal of oral and maxillofacial surgery
The use of deep learning (DL) in medical imaging is becoming increasingly widespread. Although DL has been used previously for the segmentation of facial bones in computed tomography (CT) images, there are few reports of segmentation involving multip...