AIMC Topic: Image Processing, Computer-Assisted

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A novel approach in cancer diagnosis: integrating holography microscopic medical imaging and deep learning techniques-challenges and future trends.

Biomedical physics & engineering express
Medical imaging is pivotal in early disease diagnosis, providing essential insights that enable timely and accurate detection of health anomalies. Traditional imaging techniques, such as Magnetic Resonance Imaging (MRI), Computer Tomography (CT), ult...

M2OCNN: Many-to-One Collaboration Neural Networks for simultaneously multi-modal medical image synthesis and fusion.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Acquiring comprehensive information from multi-modal medical images remains a challenge in clinical diagnostics and treatment, due to complex inter-modal dependencies and missing modalities. While cross-modal medical image s...

Deep Learning-Powered CT-Less Multitracer Organ Segmentation From PET Images: A Solution for Unreliable CT Segmentation in PET/CT Imaging.

Clinical nuclear medicine
PURPOSE: The common approach for organ segmentation in hybrid imaging relies on coregistered CT (CTAC) images. This method, however, presents several limitations in real clinical workflows where mismatch between PET and CT images are very common. Mor...

Artificial intelligence for brain neuroanatomical segmentation in magnetic resonance imaging: A literature review.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
PURPOSE: This literature review aims to synthesise current research on the application of artificial intelligence (AI) for the segmentation of brain neuroanatomical structures in magnetic resonance imaging (MRI).

A multi-scale information fusion medical image segmentation network based on convolutional kernel coupled updata mechanism.

Computers in biology and medicine
Medical image segmentation is pivotal in disease diagnosis and treatment. This paper presents a novel network architecture for medical image segmentation, termed TransDLNet, which is engineered to enhance the efficiency of multi-scale information uti...

Dilated SE-DenseNet for brain tumor MRI classification.

Scientific reports
In the field of medical imaging, particularly MRI-based brain tumor classification, we propose an advanced convolutional neural network (CNN) leveraging the DenseNet-121 architecture, enhanced with dilated convolutional layers and Squeeze-and-Excitat...

Development of a handheld GPU-assisted DSC-TransNet model for the real-time classification of plant leaf disease using deep learning approach.

Scientific reports
In agriculture, promptly and accurately identifying leaf diseases is crucial for sustainable crop production. To address this requirement, this research introduces a hybrid deep learning model that combines the visual geometric group version 19 (VGG1...

Deep learning based tractography with TractSeg in patients with hemispherotomy: Evaluation and refinement.

NeuroImage. Clinical
Deep learning-based tractography implicitly learns anatomical prior knowledge that is required to resolve ambiguities inherent in traditional streamline tractography. TractSeg is a particularly widely used example of such an approach. Even though it ...

Identification of diabetic retinopathy lesions in fundus images by integrating CNN and vision mamba models.

PloS one
Diabetic retinopathy, a retinal disorder resulting from diabetes mellitus, is a prominent cause of visual degradation and loss among the global population. Therefore, the identification and classification of diabetic retinopathy are of utmost importa...

A dual-stream model based on PRNU and quaternion RGB for detecting fake faces.

PloS one
The forensic examination of AIGC(Artificial Intelligence Generated Content) faces poses a contemporary challenge within the realm of color image forensics. A myriad of artificially generated faces by AIGC encompasses both global and local manipulatio...