AIMC Topic: Diagnostic Imaging

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CK-ATTnet: Medical image segmentation network based on convolutional kernel attention.

Computers in biology and medicine
The medical image partition model has a wide range of application prospects in medical diagnosis and treatment and has become an important auxiliary method to improve the diagnostic level by medical imaging analysis. After the feature extraction abil...

Evaluating and enhancing the robustness of vision transformers against adversarial attacks in medical imaging.

Medical & biological engineering & computing
Deep neural networks (DNNs) have demonstrated exceptional performance in medical image analysis. However, recent studies have uncovered significant vulnerabilities in DNN models, particularly their susceptibility to adversarial attacks that manipulat...

Report on the AAPM grand challenge on deep generative modeling for learning medical image statistics.

Medical physics
BACKGROUND: The findings of the 2023 AAPM Grand Challenge on Deep Generative Modeling for Learning Medical Image Statistics are reported in this Special Report.

Artificial intelligence in medical imaging education: Recommendations for undergraduate curriculum development.

Radiography (London, England : 1995)
OBJECTIVES: Artificial intelligence (AI) is rapidly being integrated into medical imaging practice, prompting calls to enhance AI education in undergraduate radiography programs. Combining evidence from literature, practitioner insights, and industry...

For the busy clinical-imaging professional in an AI world: Gaining intuition about deep learning without math.

Journal of medical imaging and radiation sciences
Medical diagnostics comprise recognizing patterns in images, tissue slides, and symptoms. Deep learning algorithms (DLs) are well suited to such tasks, but they are black boxes in various ways. To explain DL Computer-Aided Diagnostic (CAD) results an...

Current Radiology workforce perspective on the integration of artificial intelligence in clinical practice: A systematic review.

Journal of medical imaging and radiation sciences
INTRODUCTION: Artificial Intelligence (AI) represents the application of computer systems to tasks traditionally performed by humans. The medical imaging profession has experienced a transformative shift through the integration of AI. While there hav...

AI in radiology: From promise to practice - A guide to effective integration.

European journal of radiology
While Artificial Intelligence (AI) has the potential to transform the field of diagnostic radiology, important obstacles still inhibit its integration into clinical environments. Foremost among them is the inability to integrate clinical information ...

A systematic review of generalization research in medical image classification.

Computers in biology and medicine
Numerous Deep Learning (DL) classification models have been developed for a large spectrum of medical image analysis applications, which promises to reshape various facets of medical practice. Despite early advances in DL model validation and impleme...

Medical imaging and radiation science students' use of artificial intelligence for learning and assessment.

Radiography (London, England : 1995)
INTRODUCTION: Artificial intelligence has permeated all aspects of our existence, and medical imaging has shown the burgeoning use of artificial intelligence in clinical environments. However, there are limited empirical studies on radiography studen...

A review of AutoML optimization techniques for medical image applications.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Automatic analysis of medical images using machine learning techniques has gained significant importance over the years. A large number of approaches have been proposed for solving different medical image analysis tasks using machine learning and dee...