AIMC Topic: Image Processing, Computer-Assisted

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NeXtBrain: Combining local and global feature learning for brain tumor classification.

Brain research
The accurate and timely diagnosis of brain tumors is of paramount clinical significance for effective treatment planning and improved patient outcomes. While deep learning has advanced medical image analysis, concurrently achieving high classificatio...

Physics-informed neural networks for denoising high b-value diffusion-weighted images.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Diffusion-weighted imaging (DWI) is widely applied in tumor diagnosis by measuring the diffusion of water molecules. To increase the sensitivity to tumor identification, faithful high b-value DWI images are expected by setting a stronger strength of ...

Diffusion-based image translation model from low-dose chest CT to calcium scoring CT with random point sampling.

Computers in biology and medicine
BACKGROUND: Coronary artery calcium (CAC) scoring is an important method for cardiovascular risk assessment. While artificial intelligence (AI) has been applied to automate CAC scoring in calcium scoring computed tomography (CSCT), its application to...

SCAI-Net: An AI-driven framework for optimized, fast, and resource-efficient skull implant generation for cranioplasty using CT images.

Computers in biology and medicine
Skull damage caused by craniectomy or trauma necessitates accurate and precise Patient-Specific Implant (PSI) design to restore the cranial cavity. Conventional Computer-Aided Design (CAD)-based methods for PSI design are highly infrastructure-intens...

An enhanced UHMWPE wear particle detection approach based on YOLOv9.

Medical engineering & physics
Ultra-high molecular weight polyethylene (UHMWPE) has been widely used in total joint arthroplasty for orthopedic and spinal implants. However, the biological response to UHMWPE wear particles has been identified as a major contributor to inflammator...

Inconsistency of AI in intracranial aneurysm detection with varying dose and image reconstruction.

Scientific reports
Scanner-related changes in data quality are common in medical imaging, yet monitoring their impact on diagnostic AI performance remains challenging. In this study, we performed standardized consistency testing of an FDA-cleared and CE-marked AI for t...

An efficient low-shot class-agnostic counting framework with hybrid encoder and iterative exemplar feature learning.

PloS one
Few-shot learning techniques have enabled the rapid adaptation of a general AI model to various tasks using limited data. In this study, we focus on class-agnostic low-shot object counting, a challenging problem that aims to achieve accurate object c...

A method for spatial interpretation of weakly supervised deep learning models in computational pathology.

Scientific reports
Deep learning enables the modelling of high-resolution histopathology whole-slide images (WSI). Weakly supervised learning of tile-level data is typically applied for tasks where labels only exist on the patient or WSI level (e.g. patient outcomes or...

A 3D lightweight network with Roberts edge enhancement model (LR-Net) for brain tumor segmentation.

Scientific reports
In clinical medicine, a reliable and resource-friendly computer-aided diagnosis (CAD) method for brain tumor segmentation is essential to enhance diagnostic accuracy and therapeutic outcomes, particularly in regions with uneven healthcare resource di...

Enhancing pancreatic cancer detection in CT images through secretary wolf bird optimization and deep learning.

Scientific reports
The pancreas is a gland in the abdomen that helps to produce hormones and digest food. The irregular development of tissues in the pancreas is termed as pancreatic cancer. Identification of pancreatic tumors early is significant for enhancing surviva...