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Pathology

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Hierarchical deep convolutional neural networks combine spectral and spatial information for highly accurate Raman-microscopy-based cytopathology.

Journal of biophotonics
Hierarchical variants of so-called deep convolutional neural networks (DCNNs) have facilitated breakthrough results for numerous pattern recognition tasks in recent years. We assess the potential of these novel whole-image classifiers for Raman-micro...

Structuring Legacy Pathology Reports by openEHR Archetypes to Enable Semantic Querying.

Methods of information in medicine
BACKGROUND: Clinical information is often stored as free text, e.g. in discharge summaries or pathology reports. These documents are semi-structured using section headers, numbered lists, items and classification strings. However, it is still challen...

Developing the Quantitative Histopathology Image Ontology (QHIO): A case study using the hot spot detection problem.

Journal of biomedical informatics
Interoperability across data sets is a key challenge for quantitative histopathological imaging. There is a need for an ontology that can support effective merging of pathological image data with associated clinical and demographic data. To foster or...

Image analysis and machine learning in digital pathology: Challenges and opportunities.

Medical image analysis
With the rise in whole slide scanner technology, large numbers of tissue slides are being scanned and represented and archived digitally. While digital pathology has substantial implications for telepathology, second opinions, and education there are...

Automatic negation detection in narrative pathology reports.

Artificial intelligence in medicine
OBJECTIVE: To detect negations of medical entities in free-text pathology reports with different approaches, and evaluate their performances.

Generative Artificial Intelligence in Anatomic Pathology.

Archives of pathology & laboratory medicine
CONTEXT.—: Generative artificial intelligence (AI) has emerged as a transformative force in various fields, including anatomic pathology, where it offers the potential to significantly enhance diagnostic accuracy, workflow efficiency, and research ca...

Introduction to Generative Artificial Intelligence: Contextualizing the Future.

Archives of pathology & laboratory medicine
CONTEXT.—: Generative artificial intelligence (GAI) is a promising new technology with the potential to transform communication and workflows in health care and pathology. Although new technologies offer advantages, they also come with risks that use...

Harnessing the Power of Generative Artificial Intelligence in Pathology Education: Opportunities, Challenges, and Future Directions.

Archives of pathology & laboratory medicine
CONTEXT.—: Generative artificial intelligence (AI) technologies are rapidly transforming numerous fields, including pathology, and hold significant potential to revolutionize educational approaches.

Ethical and Regulatory Perspectives on Generative Artificial Intelligence in Pathology.

Archives of pathology & laboratory medicine
CONTEXT.—: Technology companies and research groups are increasingly exploring applications of generative artificial intelligence (GenAI) in pathology and laboratory medicine. Although GenAI holds considerable promise, it also introduces novel risks ...