AIMC Topic: Image Processing, Computer-Assisted

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Efficient Vision Transformers for Ophthalmic Images Classification: A Comparative Study of Supervised, Semi-Supervised, and Unsupervised Learning Approaches.

Journal of medical systems
This study explored the integration of supervised, semi-supervised, and unsupervised learning strategies to classify ophthalmic images under label-scarce conditions. Given the high cost of annotations in medical imaging, the goal was to improve diagn...

Visual language model-assisted spectral CT reconstruction by diffusion and low-rank priors from limited-angle measurements.

Physics in medicine and biology
Spectral computed tomography (CT) is a critical tool in clinical practice, offering capabilities in multi-energy spectrum imaging and material identification. The limited-angle (LA) scanning strategy has attracted attention for its advantages in fast...

Zero-shot image classification based on class representation learning and attribute embedding learning.

PloS one
Zero-shot learning (ZSL) aims to classify unseen classes by leveraging semantic information from seen classes, addressing the challenge of limited labeled data. In recent years, ZSL methods have focused on extracting attribute-level features from ima...

Fast machine learning image reconstruction of radially undersampled k-space data for low-latency real-time MRI.

PloS one
Fast data acquisition and fast image reconstruction are essential to enable low-latency real-time magnetic resonance (MR) imaging applications with high temporal resolution such as interstitial percutaneous needle interventions or MR-guided radiother...

Enhancing museum collection images with fuzzy set guided convolutional neural network: A novel approach leveraging fuzzy set theory.

PloS one
Museum collection images are invaluable for preserving cultural heritage and studying history. However, these images often lack quality and clarity. This study introduces a novel museum collection image enhancement technique based on fuzzy set theory...

Enhanced wheat crop leaf disease classification using multi-level contrast enhancement and modified vision transformers.

Scientific reports
The integration of advanced tools and techniques has significantly boosted agricultural productivity. Wheat crops, which are vital for global food security, are often susceptible to various bacterial and viral diseases, considerably impacting both yi...

Impact of image preprocessing methods on MRI radiomics feature variability and classification performance in Parkinson's disease motor subtype analysis.

Scientific reports
To evaluate the impact of various magnetic resonance imaging (MRI) preprocessing methods on radiomic feature reproducibility and classification performance in differentiating Parkinson's disease (PD) motor subtypes. We analyzed 210 T1-weighted MRI sc...

Trustworthy pneumonia detection in chest X-ray imaging through attention-guided deep learning.

Scientific reports
Pneumonia remains a significant global health threat, especially among children, the elderly, and immunocompromised individuals. Chest X-ray (CXR) imaging is commonly used for diagnosis, but manual interpretation is prone to errors and variability. T...

AI-driven software for automated quantification of skeletal metastases and treatment response evaluation using whole-body diffusion-weighted MRI (WB-DWI) in advanced prostate cancer.

Physics in medicine and biology
. Quantitative assessment of treatment response in advanced prostate cancer (APC) with bone metastases remains an unmet clinical need. Whole-body diffusion-weighted MRI (WB-DWI) provides two response biomarkers: total diffusion volume (TDV) and globa...

Impact of patient-specific deep learning lung organs-at-risk segmentation on accumulated dose in online adaptive 0.35 T MR-guided radiotherapy.

Physics in medicine and biology
Online adaptation in magnetic resonance imaging-guided radiotherapy (MRgRT) for lung cancer is hindered by time-consuming organs-at-risk (OARs) recontouring on daily MR images (dMRIs) and inter-/intra-observer variability. Deep learning auto-segmenta...