AIMC Topic: Diagnostic Imaging

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Differences in technical and clinical perspectives on AI validation in cancer imaging: mind the gap!

European radiology experimental
Good practices in artificial intelligence (AI) model validation are key for achieving trustworthy AI. Within the cancer imaging domain, attracting the attention of clinical and technical AI enthusiasts, this work discusses current gaps in AI validati...

Deep learning-based encryption scheme for medical images using DCGAN and virtual planet domain.

Scientific reports
The motivation for this article stems from the fact that medical image security is crucial for maintaining patient confidentiality and protecting against unauthorized access or manipulation. This paper presents a novel encryption technique that integ...

Unsupervised deep learning-based medical image registration: a survey.

Physics in medicine and biology
In recent decades, medical image registration technology has undergone significant development, becoming one of the core technologies in medical image analysis. With the rise of deep learning, deep learning-based medical image registration methods ha...

Uncertainty Global Contrastive Learning Framework for Semi-Supervised Medical Image Segmentation.

IEEE journal of biomedical and health informatics
In semi-supervised medical image segmentation, the issue of fuzzy boundaries for segmented objects arises. With limited labeled data and the interaction of boundaries from different segmented objects, classifying segmentation boundaries becomes chall...

Contrastive Registration for Unsupervised Medical Image Segmentation.

IEEE transactions on neural networks and learning systems
Medical image segmentation is an important task in medical imaging, as it serves as the first step for clinical diagnosis and treatment planning. While major success has been reported using deep learning supervised techniques, they assume a large and...

Artificial Intelligence in Gastrointestinal Imaging: Advances and Applications.

Radiologic clinics of North America
While artificial intelligence (AI) has shown considerable progress in many areas of medical imaging, applications in abdominal imaging, particularly for the gastrointestinal (GI) system, have notably lagged behind advancements in other body regions. ...

Automatic medical report generation based on deep learning: A state of the art survey.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
With the increasing popularity of medical imaging and its expanding applications, posing significant challenges for radiologists. Radiologists need to spend substantial time and effort to review images and manually writing reports every day. To addre...

Neural Architecture Search for biomedical image classification: A comparative study across data modalities.

Artificial intelligence in medicine
Deep neural networks have significantly advanced medical image classification across various modalities and tasks. However, manually designing these networks is often time-consuming and suboptimal. Neural Architecture Search (NAS) automates this proc...

Investigating and Improving Latent Density Segmentation Models for Aleatoric Uncertainty Quantification in Medical Imaging.

IEEE transactions on medical imaging
Data uncertainties, such as sensor noise, occlusions or limitations in the acquisition method can introduce irreducible ambiguities in images, which result in varying, yet plausible, semantic hypotheses. In Machine Learning, this ambiguity is commonl...

Artificial intelligence education in medical imaging: A scoping review.

Journal of medical imaging and radiation sciences
BACKGROUND: The rise of Artificial intelligence (AI) is reshaping healthcare, particularly in medical imaging. In this emerging field, clinical imaging personnel need proper training. However, formal AI education is lacking in medical curricula, coup...