AIMC Topic: Image Processing, Computer-Assisted

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Enhancing synthetic pelvic CT generation from CBCT using vision transformer with adaptive fourier neural operators.

Biomedical physics & engineering express
This study introduces a novel approach to improve Cone Beam CT (CBCT) image quality by developing a synthetic CT (sCT) generation method using CycleGAN with a Vision Transformer (ViT) and an Adaptive Fourier Neural Operator (AFNO).A dataset of 20 pro...

Enhancing meningioma tumor classification accuracy through multi-task learning approach and image analysis of MRI images.

PloS one
BACKGROUND: Accurate classification of meningioma brain tumors is crucial for determining the appropriate treatment plan and improving patient outcomes. However, this task is challenging due to the slow-growing nature of these tumors and the potentia...

Colour segmentation of printed fabrics by integrating adaptive neural network and density peak clustering algorithm.

PloS one
With the development of computer vision and image processing technology, color segmentation of printed fabrics has gradually become a key task in the textile industry. However, the existing methods often face the problems of low segmentation accuracy...

Impact of deep learning and post-processing algorithms performances on biodiversity metrics assessed on videos.

PloS one
Assessing the escalating biodiversity crisis, driven by climate change, habitat destruction, and exploitation, necessitates efficient monitoring strategies to assess species presence and abundance across diverse habitats. Video-based surveys using re...

Distilling knowledge from graph neural networks trained on cell graphs to non-neural student models.

Scientific reports
The development and refinement of artificial intelligence (AI) and machine learning algorithms have been an area of intense research in radiology and pathology, particularly for automated or computer-aided diagnosis. Whole Slide Imaging (WSI) has eme...

StarVasc: hyper-dimensional and spectral feature expansion for lightweight vascular enhancement.

Journal of robotic surgery
Vascular contrast enhancement is crucial for early disease diagnosis and surgical precision in robotic surgery imaging. Traditional white-light imaging often fails to distinguish blood vessels due to the spectral similarity between vessels and surrou...

Artificial intelligence with feature fusion empowered enhanced brain stroke detection and classification for disabled persons using biomedical images.

Scientific reports
Brain stroke is an illness which affects almost every age group, particularly people over 65. There are two significant kinds of strokes: ischemic and hemorrhagic strokes. Blockage of brain vessels causes an ischemic stroke, while cracks in blood ves...

Oral cancer detection via Vanilla CNN optimized by improved artificial protozoa optimizer.

Scientific reports
In this study, we propose a new method for oral cancer detection using a modified Vanilla Convolutional Neural Network (CNN) architecture with incorporated batch normalization, dropout regularization, and a customized design structure for the convolu...

BCDCNN: breast cancer deep convolutional neural network for breast cancer detection using MRI images.

Scientific reports
Breast cancer (BC) is a kind of cancer that is created from the cells in breast tissue. This is a primary cancer that occurs in women. Earlier identification of BC is significant in the treatment process. To lessen unwanted biopsies, Magnetic Resonan...

LKDA-Net: Hierarchical transformer with large Kernel depthwise convolution attention for 3D medical image segmentation.

PloS one
Since Transformers have demonstrated excellent performance in the segmentation of two-dimensional medical images, recent works have also introduced them into 3D medical segmentation tasks. For example, hierarchical transformers like Swin UNETR have r...