AIMC Topic: Image Processing, Computer-Assisted

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DERMA-OCTA: A Comprehensive Dataset and Preprocessing Pipeline for Dermatological OCTA Vessel Segmentation.

Scientific data
Optical coherence tomography angiography (OCTA) has emerged as a promising tool for non-invasive vascular imaging in dermatology. However, the field lacks standardized methods for processing and analyzing these complex images, as well as sufficient a...

Performance of deep learning models for the classification and object detection of different oral white lesions using photographic images.

Scientific reports
Computer vision adjunctive technology for oral lesion diagnoses has been developed to detect and identify Oral Potentially Malignant Disorders (OPMDs) and non-OPMDs. The early detection of OPMDs can reduce the risk of oral cancer development, improvi...

Deep unrolled primal dual network for TOF-PET list-mode image reconstruction.

Physics in medicine and biology
Time-of-flight (TOF) information provides more accurate location data for annihilation photons, thereby enhancing the quality of positron emission tomography (PET) reconstruction images and reducing noise. List-mode reconstruction has a significant a...

TomoGRAF: An X-ray physics-driven generative radiance field framework for extremely sparse view CT reconstruction.

PloS one
OBJECTIVES: Computed tomography (CT) provides high spatial-resolution visualization of 3D structures for various applications. Traditional analytical/iterative CT reconstruction algorithms require hundreds of angular samplings, a condition may not be...

Progressive decomposition of infrared and visible image fusion network with joint transformer and Resnet.

PloS one
The objective of image fusion is to synthesize information from multiple source images into a single, high-quality composite that is information-rich, thereby enhancing both human visual interpretation and machine perception capabilities. This proces...

FastDIP: An effective approach for accelerating unsupervised low-count PET image reconstruction.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
INTRODUCTION: Unsupervised deep learning methods can improve the image quality of positron emission tomography (PET) images without the need for large-scale datasets. However, these approaches typically require training a distinct network for each pa...

Integrating non-linear radon transformation for diabetic retinopathy grading.

Scientific reports
Diabetic retinopathy is a serious ocular complication that poses a significant threat to patients' vision and overall health. Early detection and accurate grading are essential to prevent vision loss. Current automatic grading methods rely heavily on...

Enhancing B-mode-based breast cancer diagnosis via cross-attention fusion of H-scan and Nakagami imaging with multi-CAM-QUS-Driven XAI.

Physics in medicine and biology
B-mode ultrasound is widely employed for breast lesion diagnosis due to its affordability, widespread availability, and effectiveness, particularly in cases of dense breast tissue where mammography may be less sensitive. However, it disregards critic...

Memory-enhanced and multi-domain learning-based deep unrolling network for medical image reconstruction.

Physics in medicine and biology
. Reconstructing high-quality images from corrupted measurements remains a fundamental challenge in medical imaging. Recently, deep unrolling (DUN) methods have emerged as a promising solution, combining the interpretability of traditional iterative ...

EchoMamba: A new Mamba model for fast and efficient hyperspectral image classification.

PloS one
The classification of hyperspectral images (HSI) is an important foundation in the field of remote sensing. Mamba architectures based on state space model (SSM) have shown great potential in the field of HSI processing due to their powerful long-rang...