AIMC Topic: Image Interpretation, Computer-Assisted

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Comparative Analysis of Artificial Intelligence Algorithms for Breast Cancer Detection from Pathological Image in Burkina Faso.

Studies in health technology and informatics
Artificial Intelligence (AI) has revolutionized many fields, including medical imaging. This revolution has enabled the digitization of medical images, the development of algorithms allowing the use of data captured in natural language, and deep lear...

Artificial Intelligence System for Automated Breast Cancer Detection in Pathology in Burkina Faso: Methodology Overview.

Studies in health technology and informatics
The introduction of artificial intelligence (AI) in breast cancer diagnosis in Burkina Faso represents a significant advancement in the field of healthcare. Faced with the public health issue posed by breast cancer, this study focuses on the use of A...

Deep Learning-Based Synthetic Skin Lesion Image Classification.

Studies in health technology and informatics
Advances in general-purpose computers have enabled the generation of high-quality synthetic medical images that human eyes cannot differ between real and AI-generated images. To analyse the efficacy of the generated medical images, this study propose...

Deep Learning Based Automatic Fibroglandular Tissue Segmentation in Breast Magnetic Resonance Imaging Screening.

Studies in health technology and informatics
In light of the global increase in breast cancer cases and the crucial importance of the density of fibroglandular tissue (FGT) in assessing risk and predicting the course of the disease, the accurate measurement of FGT emerges as a significant chall...

Assessing the Performance of Deep Learning for Automated Gleason Grading in Prostate Cancer.

Studies in health technology and informatics
Prostate cancer is a dominant health concern calling for advanced diagnostic tools. Utilizing digital pathology and artificial intelligence, this study explores the potential of 11 deep neural network architectures for automated Gleason grading in pr...

Precise Identification of Oral Cancer Lesions Using Artificial Intelligence.

Studies in health technology and informatics
Dentists, especially those who are not oral lesion specialists and live in rural areas, need an artificial intelligence (AI) system for accurately assisting them in screening for oral cancer that may appear in smartphone images. Not many literatures ...

GastroFuse-Net: an ensemble deep learning framework designed for gastrointestinal abnormality detection in endoscopic images.

Mathematical biosciences and engineering : MBE
Convolutional Neural Networks (CNNs) have received substantial attention as a highly effective tool for analyzing medical images, notably in interpreting endoscopic images, due to their capacity to provide results equivalent to or exceeding those of ...

Fully Automated Deep Learning Model to Detect Clinically Significant Prostate Cancer at MRI.

Radiology
Background Multiparametric MRI can help identify clinically significant prostate cancer (csPCa) (Gleason score ≥7) but is limited by reader experience and interobserver variability. In contrast, deep learning (DL) produces deterministic outputs. Purp...

Evaluating the efficacy of few-shot learning for GPT-4Vision in neurodegenerative disease histopathology: A comparative analysis with convolutional neural network model.

Neuropathology and applied neurobiology
AIMS: Recent advances in artificial intelligence, particularly with large language models like GPT-4Vision (GPT-4V)-a derivative feature of ChatGPT-have expanded the potential for medical image interpretation. This study evaluates the accuracy of GPT...

Large-scale parameters framework with large convolutional kernel for encoding visual fMRI activity information.

Cerebral cortex (New York, N.Y. : 1991)
Visual encoding models often use deep neural networks to describe the brain's visual cortex response to external stimuli. Inspired by biological findings, researchers found that large receptive fields built with large convolutional kernels improve co...