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HistoColAi: An open-source web platform for collaborative digital histology image annotation with AI-driven

Cristian Camilo Pulgarín-Ospina1, Rocío Del Amor1, Julio José Silva-Rodríguez2

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Summary

A new web service simplifies digital pathology by enabling annotation of whole slide images (WSI) and integrating AI insights. This tool makes advanced deep learning accessible for pathologists, aiding in diagnoses like spindle cell skin neoplasm.

Keywords:
Annotation toolDeep learningDigital pathologyWeb service

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Area of Science:

  • Digital pathology
  • Computational pathology
  • Medical image analysis

Background:

  • Digital pathology enhances workflows with high-detail whole slide images (WSI) and facilitates inter-hospital case sharing.
  • Deep learning advancements offer potential for computer-aided diagnostics in pathology.
  • A key challenge is the lack of intuitive, open-source web applications for pathology data annotation.

Purpose of the Study:

  • To propose a web service for efficient visualization and annotation of digitized histological images.
  • To integrate AI-driven predictive insights into the pathology workflow.
  • To democratize the use of deep learning models for pathologists.

Main Methods:

  • Development of a web service for annotating digitized histological images, primarily WSI in TIFF format.
  • Integration of AI-driven predictive insights.
  • Demonstration through a use case involving diagnosis of spindle cell skin neoplasm.
  • Conducting a usability study to assess feasibility.

Main Results:

  • The developed web service efficiently visualizes and annotates digitized histological images.
  • The tool integrates AI-driven predictive insights for pathology applications.
  • A usability study confirmed the feasibility of the developed tool for pathologists.

Conclusions:

  • The proposed web service addresses the challenge of data annotation in digital pathology.
  • This tool enhances accessibility and usability of deep learning models for pathologists.
  • The approach shows promise for improving diagnostic accuracy and efficiency in histopathology.