AIMC Topic: Diagnostic Imaging

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Ethical considerations and robustness of artificial neural networks in medical image analysis under data corruption.

Scientific reports
Medicine is one of the most sensitive fields in which artificial intelligence (AI) is extensively used, spanning from medical image analysis to clinical support. Specifically, in medicine, where every decision may severely affect human lives, the iss...

Facilitators and Barriers to Implementing AI in Routine Medical Imaging: Systematic Review and Qualitative Analysis.

Journal of medical Internet research
BACKGROUND: Artificial intelligence (AI) is rapidly advancing in health care, particularly in medical imaging, offering potential for improved efficiency and reduced workload. However, there is little systematic evidence on process factors for succes...

Enhancing cardiac disease detection via a fusion of machine learning and medical imaging.

Scientific reports
Cardiovascular illnesses continue to be a predominant cause of mortality globally, underscoring the necessity for prompt and precise diagnosis to mitigate consequences and healthcare expenditures. This work presents a complete hybrid methodology that...

Generative AI enables medical image segmentation in ultra low-data regimes.

Nature communications
Semantic segmentation of medical images is pivotal in applications like disease diagnosis and treatment planning. While deep learning automates this task effectively, it struggles in ultra low-data regimes for the scarcity of annotated segmentation m...

MIDAS: a technology-enabled hub-and-spoke system for the collection and dissemination of high-quality medical datasets in India.

BMC medical informatics and decision making
BACKGROUND: The need for better AI models fuels the demand for larger and larger high-quality datasets with significant diversity. Over the years, many medical imaging datasets have been published globally, but existing datasets do not contain enough...

Enhanced security for medical images using a new 5D hyper chaotic map and deep learning based segmentation.

Scientific reports
Medical image encryption is important for maintaining the confidentiality of sensitive medical data and protecting patient privacy. Contemporary healthcare systems store significant patient data in text and graphic form. This research proposes a New ...

Deep generative models for Bayesian inference on high-rate sensor data: applications in automotive radar and medical imaging.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Deep generative models (DGMs) have been studied and developed primarily in the context of natural images and computer vision. This has spurred the development of (Bayesian) methods that use these generative models for inverse problems in image restor...

Implicit neural representation for medical image reconstruction.

Physics in medicine and biology
Medical image reconstruction aims to generate high-quality images from incompletely sampled raw sensor data, which poses an ill-posed inverse problem. Traditional iterative reconstruction methods rely on prior information to empirically construct reg...

Climate-Smart Diagnostic Medical Imaging and Point-of-Care Ultrasound: An Evidence-Based Perspective.

The Journal of emergency medicine
BACKGROUND: Hospital diagnostic imaging significantly contributes to healthcare's carbon emissions, with modalities such as magnetic resonance imaging (MRI) and computed tomography (CT), accounting for a disproportionate share of energy consumption a...

Gender and Ethnicity Bias of Text-to-Image Generative Artificial Intelligence in Medical Imaging, Part 2: Analysis of DALL-E 3.

Journal of nuclear medicine technology
Disparity among gender and ethnicity remains an issue across medicine and health science. Only 26%-35% of trainee radiologists are female, despite more than 50% of medical students' being female. Similar gender disparities are evident across the medi...