AIMC Topic: Image Processing, Computer-Assisted

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A QR code-enabled framework for fast biomedical image processing in medical diagnosis using deep learning.

BMC medical imaging
In the realm of disease prognosis and diagnosis, a plethora of medical images are utilized. These images are typically stored either within the local on-premises servers of healthcare providers or within cloud storage infrastructures. However, this c...

Improved microvascular imaging with optical coherence tomography using 3D neural networks and a channel attention mechanism.

Scientific reports
Skin microvasculature is vital for human cardiovascular health and thermoregulation, but its imaging and analysis presents significant challenges. Statistical methods such as speckle decorrelation in optical coherence tomography angiography (OCTA) of...

Skin cancer detection through attention guided dual autoencoder approach with extreme learning machine.

Scientific reports
Skin cancer is a lethal disease, and its early detection plays a pivotal role in preventing its spread to other body organs and tissues. Artificial Intelligence (AI)-based automated methods can play a significant role in its early detection. This stu...

Decoding pulsatile patterns of cerebrospinal fluid dynamics through enhancing interpretability in machine learning.

Scientific reports
Analyses of complex behaviors of Cerebrospinal Fluid (CSF) have become increasingly important in diseases diagnosis. The changes of the phase-contrast magnetic resonance imaging (PC-MRI) signal formed by the velocity of flowing CSF are represented as...

Using Advanced Convolutional Neural Network Approaches to Reveal Patient Age, Gender, and Weight Based on Tongue Images.

BioMed research international
The human tongue has been long believed to be a window to provide important insights into a patient's health in medicine. The present study introduced a novel approach to predict patient age, gender, and weight inferences based on tongue images using...

Brain MRI detection and classification: Harnessing convolutional neural networks and multi-level thresholding.

PloS one
Brain tumor detection in clinical applications is a complex and challenging task due to the intricate structures of the human brain. Magnetic Resonance (MR) imaging is widely preferred for this purpose because of its ability to provide detailed image...

A Cloud-Based System for Automated AI Image Analysis and Reporting.

Journal of imaging informatics in medicine
Although numerous AI algorithms have been published, the relatively small number of algorithms used clinically is partly due to the difficulty of implementing AI seamlessly into the clinical workflow for radiologists and for their healthcare enterpri...

Automatic segmentation of the maxillary sinus on cone beam computed tomographic images with U-Net deep learning model.

European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
BACKGROUND: Medical imaging segmentation is the use of image processing techniques to expand specific structures or areas in medical images. This technique is used to separate and display different textures or shapes in an image. The aim of this stud...

A two-stream decision fusion network for cervical pap-smear image classification tasks.

Tissue & cell
Deep learning, especially Convolution Neural Networks (CNNs), has demonstrated superior performance in image recognition and classification tasks. They make complex pattern recognition possible by extracting image features through layers of abstracti...

Genome analysis through image processing with deep learning models.

Journal of human genetics
Genomic sequences are traditionally represented as strings of characters: A (adenine), C (cytosine), G (guanine), and T (thymine). However, an alternative approach involves depicting sequence-related information through image representations, such as...