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Recent Advancements in Deep Learning Using Whole Slide Imaging for Cancer Prognosis.

Minhyeok Lee1

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This summary is machine-generated.

Deep learning applied to whole slide images (WSIs) shows great promise for cancer prognosis. This review analyzes recent advancements, highlighting key developments and future research directions for improved cancer care.

Keywords:
artificial intelligencecancer prognosisdigital pathologyimage analysismachine learningmedical imagingsurvival analysiswhole slide images

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Area of Science:

  • Computational pathology
  • Digital pathology
  • Artificial intelligence in oncology

Background:

  • Whole slide images (WSIs) are increasingly available, offering rich data for computational analysis.
  • Deep learning (DL) techniques have rapidly advanced, showing potential in medical image analysis.
  • Accurate cancer prognosis is crucial for effective treatment planning and patient management.

Purpose of the Study:

  • To provide a comprehensive review of deep learning applications in cancer prognosis using WSIs.
  • To analyze advancements in DL methodologies for WSI analysis published between 2019 and 2023.
  • To critically evaluate the strengths, weaknesses, and future directions of DL in cancer prognosis.

Main Methods:

  • Systematic literature review of publications from 2019-2023.
  • Analysis of deep learning techniques applied to whole slide images for cancer prognosis.
  • Evaluation of methodologies, outcomes, and limitations of identified studies.

Main Results:

  • Deep learning models demonstrate significant potential in revolutionizing cancer prognosis prediction from WSIs.
  • Key advancements include improved feature extraction, predictive accuracy, and integration of multi-modal data.
  • Identified challenges include data heterogeneity, model interpretability, and clinical validation.

Conclusions:

  • Deep learning on WSIs is a rapidly evolving field with transformative potential for cancer prognosis.
  • Further research is needed to address current limitations and facilitate clinical translation.
  • This review serves as a critical resource for researchers and clinicians navigating this complex domain.