AIMC Topic: Diagnostic Imaging

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Perspectives of medical imaging professionals about the impact of AI on Swiss radiographers.

Journal of medical imaging and radiation sciences
INTRODUCTION: Artificial Intelligence (AI) is increasingly implemented in medical imaging practice, however, its impact on radiographers practice is not well studied. The aim of this study was to explore the perceived impact of AI on radiographers' a...

2023 Industry Perceptions Survey on AI Adoption and Return on Investment.

Journal of imaging informatics in medicine
This SIIM-sponsored 2023 report highlights an industry view on artificial intelligence adoption barriers and success related to diagnostic imaging, life sciences, and contrasts. In general, our 2023 survey indicates that there has been progress in ad...

A systematic review of few-shot learning in medical imaging.

Artificial intelligence in medicine
The lack of annotated medical images limits the performance of deep learning models, which usually need large-scale labelled datasets. Few-shot learning techniques can reduce data scarcity issues and enhance medical image analysis speed and robustnes...

Construction and Validation of a General Medical Image Dataset for Pretraining.

Journal of imaging informatics in medicine
In the field of deep learning for medical image analysis, training models from scratch are often used and sometimes, transfer learning from pretrained parameters on ImageNet models is also adopted. However, there is no universally accepted medical im...

AI-powered innovations in pancreatitis imaging: a comprehensive literature synthesis.

Abdominal radiology (New York)
Early identification of pancreatitis remains a significant clinical diagnostic challenge that impacts patient outcomes. The evolution of quantitative imaging followed by deep learning models has shown great promise in the non-invasive diagnosis of pa...

UC-Hybrid: Uncertainty-based contrastive learning on hybrid network for medical image segmentation.

Computer methods and programs in biomedicine
Medical image segmentation has made remarkable progress with advances in deep learning technology, depending on the quality and quantity of labeled data. Although various deep learning model structures and training methods have been proposed and high...

A QR code-enabled framework for fast biomedical image processing in medical diagnosis using deep learning.

BMC medical imaging
In the realm of disease prognosis and diagnosis, a plethora of medical images are utilized. These images are typically stored either within the local on-premises servers of healthcare providers or within cloud storage infrastructures. However, this c...

Evaluating artificial intelligence for medical imaging: a primer for clinicians.

British journal of hospital medicine (London, England : 2005)
Artificial intelligence has the potential to transform medical imaging. The effective integration of artificial intelligence into clinical practice requires a robust understanding of its capabilities and limitations. This paper begins with an overvie...

Multi-rater Prism: Learning self-calibrated medical image segmentation from multiple raters.

Science bulletin
In medical image segmentation, it is often necessary to collect opinions from multiple experts to make the final decision. This clinical routine helps to mitigate individual bias. However, when data is annotated by multiple experts, standard deep lea...