AIMC Topic: Image Processing, Computer-Assisted

Clear Filters Showing 2591 to 2600 of 10288 articles

Identifying radiogenomic associations of breast cancer based on DCE-MRI by using Siamese Neural Network with manufacturer bias normalization.

Medical physics
BACKGROUND AND PURPOSE: The immunohistochemical test (IHC) for Human Epidermal Growth Factor Receptor 2 (HER2) and hormone receptors (HR) provides prognostic information and guides treatment for patients with invasive breast cancer. The objective of ...

Tongue image fusion and analysis of thermal and visible images in diabetes mellitus using machine learning techniques.

Scientific reports
The study aimed to achieve the following objectives: (1) to perform the fusion of thermal and visible tongue images with various fusion rules of discrete wavelet transform (DWT) to classify diabetes and normal subjects; (2) to obtain the statistical ...

Enhancing semantic segmentation in chest X-ray images through image preprocessing: ps-KDE for pixel-wise substitution by kernel density estimation.

PloS one
BACKGROUND: In medical imaging, the integration of deep-learning-based semantic segmentation algorithms with preprocessing techniques can reduce the need for human annotation and advance disease classification. Among established preprocessing techniq...

Image capturing, segmentation and data analysis of shredded refuse streams.

Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA
Refuse sorting is an important cornerstone of the recycling industry, but ever-changing refuse compositions and the desire to increase recycling rates still pose many unsolved challenges. The digitalisation of refuse sorting plants promises to overco...

Image-based facial emotion recognition using convolutional neural network on emognition dataset.

Scientific reports
Detecting emotions from facial images is difficult because facial expressions can vary significantly. Previous research on using deep learning models to classify emotions from facial images has been carried out on various datasets that contain a limi...

Deep Learning-Enabled Automated Quality Control for Liver MR Elastography: Initial Results.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Several factors can impair image quality and reliability of liver magnetic resonance elastography (MRE), such as inadequate driver positioning, insufficient wave propagation and patient-related factors.

NFMPAtt-Unet: Neighborhood Fuzzy C-means Multi-scale Pyramid Hybrid Attention Unet for medical image segmentation.

Neural networks : the official journal of the International Neural Network Society
Medical image segmentation is crucial for understanding anatomical or pathological changes, playing a key role in computer-aided diagnosis and advancing intelligent healthcare. Currently, important issues in medical image segmentation need to be addr...

Reducing annotating load: Active learning with synthetic images in surgical instrument segmentation.

Medical image analysis
Accurate instrument segmentation in the endoscopic vision of minimally invasive surgery is challenging due to complex instruments and environments. Deep learning techniques have shown competitive performance in recent years. However, deep learning us...

Computer-aided diagnosis of cystic lung diseases using CT scans and deep learning.

Medical physics
BACKGROUND: Auxiliary diagnosis of different types of cystic lung diseases (CLDs) is important in the clinic and is instrumental in facilitating early and specific treatments. Current clinical methods heavily depend on accumulated experience, restric...