AIMC Topic: Image Processing, Computer-Assisted

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Self-attention learning network for face super-resolution.

Neural networks : the official journal of the International Neural Network Society
Existing face super-resolution methods depend on deep convolutional networks (DCN) to recover high-quality reconstructed images. They either acquire information in a single space by designing complex models for direct reconstruction, or employ additi...

A multi-task convolutional neural network for classification and segmentation of chronic venous disorders.

Scientific reports
Chronic Venous Disorders (CVD) of the lower limbs are one of the most prevalent medical conditions, affecting 35% of adults in Europe and North America. Due to the exponential growth of the aging population and the worsening of CVD with age, it is ex...

AI-based MRI auto-segmentation of brain tumor in rodents, a multicenter study.

Acta neuropathologica communications
Automatic segmentation of rodent brain tumor on magnetic resonance imaging (MRI) may facilitate biomedical research. The current study aims to prove the feasibility for automatic segmentation by artificial intelligence (AI), and practicability of AI-...

Mandible segmentation from CT data for virtual surgical planning using an augmented two-stepped convolutional neural network.

International journal of computer assisted radiology and surgery
PURPOSE: For computer-aided planning of facial bony surgery, the creation of high-resolution 3D-models of the bones by segmenting volume imaging data is a labor-intensive step, especially as metal dental inlays or implants cause severe artifacts that...

Histogram of Oriented Gradients meet deep learning: A novel multi-task deep network for 2D surgical image semantic segmentation.

Medical image analysis
We present our novel deep multi-task learning method for medical image segmentation. Existing multi-task methods demand ground truth annotations for both the primary and auxiliary tasks. Contrary to it, we propose to generate the pseudo-labels of an ...

Deep learning prediction of diffusion MRI data with microstructure-sensitive loss functions.

Medical image analysis
Deep learning prediction of diffusion MRI (DMRI) data relies on the utilization of effective loss functions. Existing losses typically measure the signal-wise differences between the predicted and target DMRI data without considering the quality of d...

MT-nCov-Net: A Multitask Deep-Learning Framework for Efficient Diagnosis of COVID-19 Using Tomography Scans.

IEEE transactions on cybernetics
The localization and segmentation of the novel coronavirus disease of 2019 (COVID-19) lesions from computerized tomography (CT) scans are of great significance for developing an efficient computer-aided diagnosis system. Deep learning (DL) has emerge...

Neural Network in the Analysis of the MR Signal as an Image Segmentation Tool for the Determination of T and T Relaxation Times with Application to Cancer Cell Culture.

International journal of molecular sciences
Artificial intelligence has been entering medical research. Today, manufacturers of diagnostic instruments are including algorithms based on neural networks. Neural networks are quickly entering all branches of medical research and beyond. Analyzing ...

Automatic segmentation and measurement of pressure injuries using deep learning models and a LiDAR camera.

Scientific reports
Pressure injuries are a common problem resulting in poor prognosis, long-term hospitalization, and increased medical costs in an aging society. This study developed a method to do automatic segmentation and area measurement of pressure injuries using...

Low-count whole-body PET/MRI restoration: an evaluation of dose reduction spectrum and five state-of-the-art artificial intelligence models.

European journal of nuclear medicine and molecular imaging
PURPOSE: To provide a holistic and complete comparison of the five most advanced AI models in the augmentation of low-dose F-FDG PET data over the entire dose reduction spectrum.