AIMC Topic: Pathology

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Hidden Variables in Deep Learning Digital Pathology and Their Potential to Cause Batch Effects: Prediction Model Study.

Journal of medical Internet research
BACKGROUND: An increasing number of studies within digital pathology show the potential of artificial intelligence (AI) to diagnose cancer using histological whole slide images, which requires large and diverse data sets. While diversification may re...

Artificial intelligence in dermatopathology: Diagnosis, education, and research.

Journal of cutaneous pathology
Artificial intelligence (AI) utilizes computer algorithms to carry out tasks with human-like intelligence. Convolutional neural networks, a type of deep learning AI, can classify basal cell carcinoma, seborrheic keratosis, and conventional nevi, high...

Challenges in the Development, Deployment, and Regulation of Artificial Intelligence in Anatomic Pathology.

The American journal of pathology
Significant advances in artificial intelligence (AI), deep learning, and other machine-learning approaches have been made in recent years, with applications found in almost every industry, including health care. AI is capable of completing a spectrum...

Challenges Developing Deep Learning Algorithms in Cytology.

Acta cytologica
BACKGROUND: The incorporation of digital pathology into routine pathology practice is becoming more widespread. Definite advantages exist with respect to the implementation of artificial intelligence (AI) and deep learning in pathology, including cyt...

Guidelines for clinical trials using artificial intelligence - SPIRIT-AI and CONSORT-AI.

The Journal of pathology
The rapidly growing use of artificial intelligence in pathology presents a challenge in terms of study reporting and methodology. The existing guidelines for the design (SPIRIT) and reporting (CONSORT) of clinical trials have been extended with the a...

How much off-the-shelf knowledge is transferable from natural images to pathology images?

PloS one
Deep learning has achieved a great success in natural image classification. To overcome data-scarcity in computational pathology, recent studies exploit transfer learning to reuse knowledge gained from natural images in pathology image analysis, aimi...

Current and future applications of artificial intelligence in pathology: a clinical perspective.

Journal of clinical pathology
During the last decade, a dramatic rise in the development and application of artificial intelligence (AI) tools for use in pathology services has occurred. This trend is often expected to continue and reshape the field of pathology in the coming yea...

The future of pathology is digital.

Pathology, research and practice
Information, archives, and intelligent artificial systems are part of everyday life in modern medicine. They already support medical staff by mapping their workflows with shared availability of cases' referral information, as needed for example, by t...

Interpretable multimodal deep learning for real-time pan-tissue pan-disease pathology search on social media.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
Pathologists are responsible for rapidly providing a diagnosis on critical health issues. Challenging cases benefit from additional opinions of pathologist colleagues. In addition to on-site colleagues, there is an active worldwide community of patho...