AIMC Topic: Diagnostic Imaging

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What makes a good scientific presentation on artificial intelligence in medical imaging?

Clinical imaging
PURPOSE: Adequate communication of scientific findings is crucial to enhance knowledge transfer. This study aimed to determine the key features of a good scientific oral presentation on artificial intelligence (AI) in medical imaging.

A review of uncertainty quantification in medical image analysis: Probabilistic and non-probabilistic methods.

Medical image analysis
The comprehensive integration of machine learning healthcare models within clinical practice remains suboptimal, notwithstanding the proliferation of high-performing solutions reported in the literature. A predominant factor hindering widespread adop...

Artificial intelligence in veterinary diagnostic imaging: Perspectives and limitations.

Research in veterinary science
The field of veterinary diagnostic imaging is undergoing significant transformation with the integration of artificial intelligence (AI) tools. This manuscript provides an overview of the current state and future prospects of AI in veterinary diagnos...

On the evaluation of deep learning interpretability methods for medical images under the scope of faithfulness.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Evaluating the interpretability of Deep Learning models is crucial for building trust and gaining insights into their decision-making processes. In this work, we employ class activation map based attribution methods in a set...

Applications of Artificial Intelligence for Pediatric Cancer Imaging.

AJR. American journal of roentgenology
Artificial intelligence (AI) is transforming the medical imaging of adult patients. However, its utilization in pediatric oncology imaging remains constrained, in part due to the inherent scarcity of data associated with childhood cancers. Pediatric ...

Establishing a Validation Infrastructure for Imaging-Based Artificial Intelligence Algorithms Before Clinical Implementation.

Journal of the American College of Radiology : JACR
With promising artificial intelligence (AI) algorithms receiving FDA clearance, the potential impact of these models on clinical outcomes must be evaluated locally before their integration into routine workflows. Robust validation infrastructures are...

A comprehensive survey on deep active learning in medical image analysis.

Medical image analysis
Deep learning has achieved widespread success in medical image analysis, leading to an increasing demand for large-scale expert-annotated medical image datasets. Yet, the high cost of annotating medical images severely hampers the development of deep...

A Comparative Review of Imaging Journal Policies for Use of AI in Manuscript Generation.

Academic radiology
RATIONALE AND OBJECTIVES: Artificial intelligence (AI) technologies are rapidly evolving and offering new advances almost on a day-by-day basis, including various tools for manuscript generation and modification. On the other hand, these potentially ...

AI-enhanced integration of genetic and medical imaging data for risk assessment of Type 2 diabetes.

Nature communications
Type 2 diabetes (T2D) presents a formidable global health challenge, highlighted by its escalating prevalence, underscoring the critical need for precision health strategies and early detection initiatives. Leveraging artificial intelligence, particu...

Shifting to machine supervision: annotation-efficient semi and self-supervised learning for automatic medical image segmentation and classification.

Scientific reports
Advancements in clinical treatment are increasingly constrained by the limitations of supervised learning techniques, which depend heavily on large volumes of annotated data. The annotation process is not only costly but also demands substantial time...