AIMC Topic: Diagnostic Imaging

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Review of Deep Learning Approaches for Interleaved Photoacoustic and Ultrasound (PAUS) Imaging.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Photoacoustic (PA) imaging provides optical contrast at relatively large depths within the human body, compared to other optical methods, at ultrasound (US) spatial resolution. By integrating real-time PA and US (PAUS) modalities, PAUS imaging has th...

Current applications of algorithmic artificial intelligence in interventional radiology: A review of the literature.

Journal of medical imaging and radiation oncology
Artificial intelligence is a rapidly evolving area of technology whose integration into healthcare delivery infrastructure is predicted to have profound implications for medicine delivery in the 21st century. Artificial intelligence as it relates to ...

Fusion Modeling: Combining Clinical and Imaging Data to Advance Cardiac Care.

Circulation. Cardiovascular imaging
In addition to the traditional clinical risk factors, an increasing amount of imaging biomarkers have shown value for cardiovascular risk prediction. Clinical and imaging data are captured from a variety of data sources during multiple patient encoun...

Economic and Environmental Costs of Cloud Technologies for Medical Imaging and Radiology Artificial Intelligence.

Journal of the American College of Radiology : JACR
Radiology is on the verge of a technological revolution driven by artificial intelligence (including large language models), which requires robust computing and storage capabilities, often beyond the capacity of current non-cloud-based informatics sy...

Medical imaging and multimodal artificial intelligence models for streamlining and enhancing cancer care: opportunities and challenges.

Expert review of anticancer therapy
INTRODUCTION: Artificial intelligence (AI) has the potential to transform oncologic care. There have been significant developments in AI applications in medical imaging and increasing interest in multimodal models. These are likely to enable improved...

Medical image identification methods: A review.

Computers in biology and medicine
The identification of medical images is an essential task in computer-aided diagnosis, medical image retrieval and mining. Medical image data mainly include electronic health record data and gene information data, etc. Although intelligent imaging pr...

Dynamic Corrected Split Federated Learning With Homomorphic Encryption for U-Shaped Medical Image Networks.

IEEE journal of biomedical and health informatics
U-shaped networks have become prevalent in various medical image tasks such as segmentation, and restoration. However, most existing U-shaped networks rely on centralized learning which raises privacy concerns. To address these issues, federated lear...

Efficient adversarial debiasing with concept activation vector - Medical image case-studies.

Journal of biomedical informatics
BACKGROUND: A major hurdle for the real time deployment of the AI models is ensuring trustworthiness of these models for the unseen population. More often than not, these complex models are black boxes in which promising results are generated. Howeve...

Artificial intelligence in medical imaging is a tool for clinical routine and scientific discovery.

Seminars in arthritis and rheumatism
The emergence of powerful machine learning methodology together with an increasing amount of data collected during clinical routine have fostered a growing role of artificial intelligence (AI) in medicine. Algorithms have become part of clinical care...

Digital image analysis and artificial intelligence in pathology diagnostics-the Swiss view.

Pathologie (Heidelberg, Germany)
Digital pathology (DP) is increasingly entering routine clinical pathology diagnostics. As digitization of the routine caseload advances, implementation of digital image analysis algorithms and artificial intelligence tools becomes not only attainabl...