AIMC Topic: Diagnostic Imaging

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Escarcitys: A framework for enhancing medical image classification performance in scarcity of trainable samples scenarios.

Neural networks : the official journal of the International Neural Network Society
In the field of healthcare, the acquisition and annotation of medical images present significant challenges, resulting in a scarcity of trainable samples. This data limitation hinders the performance of deep learning models, creating bottlenecks in c...

MDEU-Net: Medical Image Segmentation Network Based on Multi-Head Multi-Scale Cross-Axis.

Sensors (Basel, Switzerland)
Significant advances have been made in the application of attention mechanisms to medical image segmentation, and these advances are notably driven by the development of the cross-axis attention mechanism. However, challenges remain in handling compl...

Medical image translation with deep learning: Advances, datasets and perspectives.

Medical image analysis
Traditional medical image generation often lacks patient-specific clinical information, limiting its clinical utility despite enhancing downstream task performance. In contrast, medical image translation precisely converts images from one modality to...

Integrating AI into medical imaging curricula: Insights from UK HEIs.

Radiography (London, England : 1995)
INTRODUCTION: With artificial intelligence (AI) becoming increasingly integrated into medical imaging, the Health and Care Professions Council (HCPC) updated its Standards of Proficiency for Radiographers in Autumn 2023. These changes require clinici...

Federated Learning in radiomics: A comprehensive meta-survey on medical image analysis.

Computer methods and programs in biomedicine
Federated Learning (FL) has emerged as a promising approach for collaborative medical image analysis while preserving data privacy, making it particularly suitable for radiomics tasks. This paper presents a systematic meta-analysis of recent surveys ...

A systematic literature review: exploring the challenges of ensemble model for medical imaging.

BMC medical imaging
BACKGROUND: Medical imaging has been essential and has provided clinicians with useful information about the human body to diagnose various health issues. Early diagnosis of diseases based on medical imaging can mitigate the risk of severe consequenc...

Use of Open-Source Large Language Models for Automatic Synthesis of the Entire Imaging Medical Records of Patients: A Feasibility Study.

Tomography (Ann Arbor, Mich.)
BACKGROUND/OBJECTIVES: Reviewing the entire history of imaging exams of a single patient's records is an essential step in clinical practice, but it is time and resource consuming, with potential negative effects on workflow and on the quality of med...

Principles for enhancing trust in artificial intelligence systems among medical imaging professionals in Ghana: A nationwide cross-sectional study.

Radiography (London, England : 1995)
INTRODUCTION: To realise the full potential of artificial intelligence (AI) systems in medical imaging, it is crucial to address challenges, such as cyberterrorism to foster trust and acceptance. This study aimed to determine the principles that enha...

Achieving flexible fairness metrics in federated medical imaging.

Nature communications
The rapid adoption of Artificial Intelligence (AI) in medical imaging raises fairness and privacy concerns across demographic groups, especially in diagnosis and treatment decisions. While federated learning (FL) offers decentralized privacy preserva...

Workload of diagnostic radiologists in the foreseeable future based on recent (2024) scientific advances: Updated growth expectations.

European journal of radiology
PURPOSE: To assess the expected impact of the 2024 medical imaging literature on the workload of diagnostic radiologists.