AIMC Topic: Diagnostic Imaging

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Advancing Medical Imaging Research Through Standardization: The Path to Rapid Development, Rigorous Validation, and Robust Reproducibility.

Investigative radiology
Artificial intelligence (AI) has made significant advances in radiology. Nonetheless, challenges in AI development, validation, and reproducibility persist, primarily due to the lack of high-quality, large-scale, standardized data across the world. A...

Summary of the National Cancer Institute 2023 Virtual Workshop on Medical Image De-identification-Part 2: Pathology Whole Slide Image De-identification, De-facing, the Role of AI in Image De-identification, and the NCI MIDI Datasets and Pipeline.

Journal of imaging informatics in medicine
De-identification of medical images intended for research is a core requirement for data sharing initiatives, particularly as the demand for data for artificial intelligence (AI) applications grows. The Center for Biomedical Informatics and Informati...

From vision to text: A comprehensive review of natural image captioning in medical diagnosis and radiology report generation.

Medical image analysis
Natural Image Captioning (NIC) is an interdisciplinary research area that lies within the intersection of Computer Vision (CV) and Natural Language Processing (NLP). Several works have been presented on the subject, ranging from the early template-ba...

From Diagnosis to Precision Surgery: The Transformative Role of Artificial Intelligence in Urologic Imaging.

Journal of endourology
The multidisciplinary nature of artificial intelligence (AI) has allowed for rapid growth of its application in medical imaging. Artificial intelligence algorithms can augment various imaging modalities, such as X-rays, CT, and MRI, to improve image ...

Bias in artificial intelligence for medical imaging: fundamentals, detection, avoidance, mitigation, challenges, ethics, and prospects.

Diagnostic and interventional radiology (Ankara, Turkey)
Although artificial intelligence (AI) methods hold promise for medical imaging-based prediction tasks, their integration into medical practice may present a double-edged sword due to bias (i.e., systematic errors). AI algorithms have the potential to...

Assessing Laterality Errors in Radiology: Comparing Generative Artificial Intelligence and Natural Language Processing.

Journal of the American College of Radiology : JACR
PURPOSE: We compared the performance of generative artificial intelligence (AI) (Augmented Transformer Assisted Radiology Intelligence [ATARI, Microsoft Nuance, Microsoft Corporation, Redmond, Washington]) and natural language processing (NLP) tools ...

Multi-branch CNN and grouping cascade attention for medical image classification.

Scientific reports
Visual Transformers(ViT) have made remarkable achievements in the field of medical image analysis. However, ViT-based methods have poor classification results on some small-scale medical image classification datasets. Meanwhile, many ViT-based models...

The limits of fair medical imaging AI in real-world generalization.

Nature medicine
As artificial intelligence (AI) rapidly approaches human-level performance in medical imaging, it is crucial that it does not exacerbate or propagate healthcare disparities. Previous research established AI's capacity to infer demographic data from c...

ISLE: An Intelligent Streaming Framework for High-Throughput AI Inference in Medical Imaging.

Journal of imaging informatics in medicine
As the adoption of artificial intelligence (AI) systems in radiology grows, the increase in demand for greater bandwidth and computational resources can lead to greater infrastructural costs for healthcare providers and AI vendors. To that end, we de...

NFMPAtt-Unet: Neighborhood Fuzzy C-means Multi-scale Pyramid Hybrid Attention Unet for medical image segmentation.

Neural networks : the official journal of the International Neural Network Society
Medical image segmentation is crucial for understanding anatomical or pathological changes, playing a key role in computer-aided diagnosis and advancing intelligent healthcare. Currently, important issues in medical image segmentation need to be addr...