AIMC Topic: Image Processing, Computer-Assisted

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AI detectors are poor western blot classifiers: a study of accuracy and predictive values.

PeerJ
The recent rise of generative artificial intelligence (AI) capable of creating scientific images presents a challenge in the fight against academic fraud. This study evaluates the efficacy of three free web-based AI detectors in identifying AI-genera...

Robust and generalizable artificial intelligence for multi-organ segmentation in ultra-low-dose total-body PET imaging: a multi-center and cross-tracer study.

European journal of nuclear medicine and molecular imaging
PURPOSE: Positron Emission Tomography (PET) is a powerful molecular imaging tool that visualizes radiotracer distribution to reveal physiological processes. Recent advances in total-body PET have enabled low-dose, CT-free imaging; however, accurate o...

Navigating prevalence shifts in image analysis algorithm deployment.

Medical image analysis
Domain gaps are significant obstacles to the clinical implementation of machine learning (ML) solutions for medical image analysis. Although current research emphasizes new training methods and network architectures, the specific impact of prevalence...

Architecture Knowledge Distillation for Evolutionary Generative Adversarial Network.

International journal of neural systems
Generative Adversarial Networks (GANs) are effective for image generation, but their unstable training limits broader applications. Additionally, neural architecture search (NAS) for GANs with one-shot models often leads to insufficient subnet traini...

Protocol for AI-based segmentation and quantification of interstitial cells of Cajal in murine gastric muscle.

STAR protocols
Interstitial cells of Cajal (ICCs), pacemaker and neuromodulator cells in the gastrointestinal (GI) tract, play an important role in GI motility. However, quantifying ICCs is challenging due to their mixed morphologies. Here, we present a protocol fo...

Ensemble fuzzy deep learning for brain tumor detection.

Scientific reports
This research presents a novel ensemble fuzzy deep learning approach for brain Magnetic Resonance Imaging (MRI) analysis, aiming to improve the segmentation of brain tissues and abnormalities. The method integrates multiple components, including dive...

Deep learning-based video-level view classification of two-dimensional transthoracic echocardiography.

Biomedical physics & engineering express
In recent years, deep learning (DL)-based automatic view classification of 2D transthoracic echocardiography (TTE) has demonstrated strong performance, but has not fully addressed key clinical requirements such as view coverage, classification accura...

TGF-Net: Transformer and gist CNN fusion network for multi-modal remote sensing image classification.

PloS one
In the field of earth sciences and remote exploration, the classification and identification of surface materials on earth have been a significant research area that poses considerable challenges in recent times. Although deep learning technology has...

IRMA: Machine learning-based harmonization of F-FDG PET brain scans in multi-center studies.

European journal of nuclear medicine and molecular imaging
PURPOSE: Center-specific effects in PET brain scans arise due to differences in technical and procedural aspects. This restricts the merging of data between centers and introduces source-specific bias.

Deep learning-based time-of-flight (ToF) enhancement of non-ToF PET scans for different radiotracers.

European journal of nuclear medicine and molecular imaging
AIM: To evaluate a deep learning-based time-of-flight (DLToF) model trained to enhance the image quality of non-ToF PET images for different tracers, reconstructed using BSREM algorithm, towards ToF images.