AIMC Topic: Image Processing, Computer-Assisted

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Intelligent retinal disease detection using deep learning.

Scientific reports
The rising prevalence of retinal diseases is a significant concern, as certain untreated conditions can lead to severe vision impairment or even blindness. Deep learning algorithms have emerged as a powerful tool for the diagnosis and analysis of med...

MR-based synthetic CT generation using dual-attention enhanced 3D Conditional GAN for head and neck radiotherapy.

Biomedical physics & engineering express
. This study aims to synthesize CT from MR images for radiotherapy planning of head and neck tumor using an improved three-dimensional conditional generative adversarial network (3D cGAN) based on dual-attention modules.. A total of 212 paired CT and...

Building extraction from remote sensing imagery using SegFormer with post-processing optimization.

PloS one
Traditional methods for building extraction from remote sensing images rely on feature classification techniques, which often suffer from high usage thresholds, cumbersome data processing, slow recognition speeds, and poor adaptability. With the rapi...

An automated classification of brain white matter inherited disorders (Leukodystrophy) using MRI image features.

Biomedical physics & engineering express
Leukodystrophies are a group of inherited disorders that predominantly and selectively affect the white matter of the central nervous system. Their overlapping clinical and imaging manifestations make a timely and accurate diagnosis challenging. In t...

FBFormer: interventional ultra-sparse CT reconstruction with image prior using feature back-projection and transformer.

Physics in medicine and biology
. CT-guided interventional procedures hold a significant position in clinical practice. However, due to the high number of scans and prolonged procedure times, patients are exposed to considerable radiation doses. This study aims to utilize intraoper...

Brain tumour segmentation in fused MRI-PET images with permutate U-Net framework.

PloS one
Brain tumor segmentation from MRI's and PET has always been a challenging and time-consuming phase for radiologists, due to low sensitivity boundary region pixels in this image modality. Deep learning-based image segmentation is the hot research topi...

A multi-technique ensemble model leveraging attention mechanism and image processing for enhanced colorectal tumor detection.

Scientific reports
This research introduces an improved method for identifying colorectal tumors through a combination of deep convolutional neural networks (CNNs), transfer learning, and sophisticated image processing techniques used on histopathological images. The s...

Hybrid radiomic-HOG ensemble model for accurate pulmonary nodule diagnosis.

Biomedical physics & engineering express
Lung cancer remains one of the deadliest forms of cancer worldwide, making early and accurate pulmonary-nodule classification essential for improving patient prognosis. This study presents a robust ensemble-stacking framework that integrates Histogra...

Towards real-time non-invasive detection of hyperlipidemia through finger pulse image analysis using deep learning.

Biomedical physics & engineering express
Hyperlipidemia detection involves invasive, time-consuming procedures requiring clinical laboratories and blood samples. Often asymptomatic in its early stages, hyperlipidemia significantly increases the risk of cardiovascular diseases. The objective...

PSMA PET Evaluation with a Deep Learning Platform Compared with a Standard Image Viewer and Histopathology.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Standardized prostate-specific membrane antigen (PSMA) PET/CT evaluation and reporting was introduced to aid interpretation, reproducibility, and communication. Artificial intelligence may enhance these efforts. This study aimed to evaluate the perfo...