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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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Artificial Intelligence in Lung Cancer Pathology Image Analysis.

Shidan Wang1, Donghan M Yang2, Ruichen Rong3

  • 1Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA. Shidan.Wang@utsw.edu.

Cancers
|October 31, 2019
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) and deep learning show promise for analyzing lung cancer pathology images, aiding diagnosis and prognosis. Future directions include advanced AI techniques to improve patient care in digital pathology.

Keywords:
computer-aided diagnosisdeep learningdigital pathologylung cancerpathology imagewhole-slide imaging

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Area of Science:

  • Digital pathology
  • Artificial intelligence in oncology
  • Medical imaging analysis

Background:

  • Accurate lung cancer diagnosis and prognosis are critical for treatment planning.
  • Whole slide imaging (WSI) is increasingly used in clinical pathology.
  • Computer-aided diagnosis faces challenges in pathology image analysis.

Purpose of the Study:

  • To review current and potential applications of AI in lung cancer pathology image analysis.
  • To highlight the impact of deep learning on digital pathology for lung cancer.
  • To summarize AI applications in lung cancer diagnosis and prognosis.

Main Methods:

  • Review of artificial intelligence and deep learning methods.
  • Focus on applications in pathology image analysis for lung cancer.
  • Discussion of challenges and opportunities in the field.

Main Results:

  • Deep learning demonstrates significant potential in pathology image analysis tasks.
  • AI applications cover tumor identification, prognosis prediction, and metastasis detection.
  • Current deep learning algorithms are being applied to lung cancer diagnosis and prognosis.

Conclusions:

  • Digital pathology, powered by AI, holds great potential for improving lung cancer patient care.
  • Future research directions include multi-task learning, transfer learning, and model interpretation.
  • AI advancements are poised to revolutionize lung cancer diagnosis and treatment planning.