AIMC Topic: Image Processing, Computer-Assisted

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A review of the application of deep learning in thyroid nodule imaging: from model architectures to training methods and core image analysis tasks.

Biomedical physics & engineering express
Thyroid nodules are highly prevalent in clinical practice, and their incidence has been steadily increasing in recent years, posing significant threats to human health. Traditional imaging examinations for thyroid nodules rely heavily on physicians' ...

Efficient 4D fMRI analysis via spatio-temporal screening and region-aware feature extraction for template-free brain disorder classification.

Physics in medicine and biology
Functional magnetic resonance imaging (fMRI) is crucial for identifying neurological disorder biomarkers, but current deep learning methods face some limitations. Template-dependent methods reliant on fixed brain atlases lack inter-subject specificit...

Automated forest land division using deep learning and drone imagery.

PloS one
This paper proposes an automated solution for tree enumeration in areas designated for forest land division using drone image processing. Traditional tree counting methods are time-consuming and error-prone. Our approach leverages drone imagery and a...

Contrast limited adaptive histogram equalization (CLAHE) and colour difference histogram (CDH) feature merging capsule network (CCFMCapsNet) for complex image recognition.

PloS one
To enhance crop yield, detecting leaf diseases has become a crucial research focus. Deep learning and computer vision excel in digital image processing. Various techniques grounded in deep learning have been utilized for detecting plant leaf diseases...

Enhancing lymphoma cancer detection using deep transfer learning on histopathological images.

Scientific reports
Lymphoma histopathological diagnosis is complex due to rare subtypes, morphological overlaps, and poor tumor differentiation. In this paper, an AI-based system using deep transfer learning and simulated federated learning is developed to classify two...

Generative augmentations for improved cardiac ultrasound segmentation using diffusion models.

Scientific reports
One of the main challenges in current research on segmentation in cardiac ultrasound is the lack of large and varied labeled datasets and the differences in annotation conventions between datasets. This makes it difficult to design robust segmentatio...

MDFormer: a multi-scale dense dilated transformer model for 3D medical image segmentation.

Scientific reports
To improve the precision of medical image segmentation for enhanced clinical diagnosis and treatment, this study focuses on overcoming the limitations of existing models in capturing multi-scale information under resolution constraints while maintain...

In-silico comparison of a diffusion model with conventionally trained deep networks for translating 64mT to 3T brain FLAIR.

Scientific reports
Deep learning (DL) methods are increasingly applied to address the low signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of low-field MRI (LFMRI). This study evaluates the potential of diffusion models for LFMRI enhancement, comparing the...

Artificial Intelligence-Assisted Image Extraction in Neonatal Echocardiography for Congenital Heart Disease Diagnosis in Sub-Saharan Africa: Protocol for Model Development.

JMIR research protocols
BACKGROUND: Sub-Saharan Africa (SSA) bears the highest global burden of under-5 mortality, with congenital heart disease (CHD) as a major contributor. Despite advancements in high-income countries, CHD-related mortality in SSA remains largely unchang...

Spec2VolCAMU-Net: a spectrogram-to-volume model for EEG-to-fMRI reconstruction based on Multi-directional Time-Frequency Convolutional Attention Encoder and Vision-Mamba U-Net.

Journal of neural engineering
High-resolution functional magnetic resonance imaging (fMRI) is essential for mapping human brain activity; however, it remains costly and logistically challenging. If comparable volumes could be generated directly from widely available scalp electro...