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Summary

This summary is machine-generated.

This study introduces a feedback loop for digital pathology, enhancing classification algorithms with pathologist input. This novel approach significantly improves diagnostic accuracy in computational pathology.

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Area of Science:

Computational pathology
Medical artificial intelligence
Image analysis

Background:

Digital pathology integrates classification algorithms, Graphical User Interfaces (GUIs), and pathologists.
Current systems feature unidirectional interaction from algorithms to pathologists.
Improving algorithm performance necessitates incorporating pathologist expertise.

Purpose of the Study:

To introduce a novel feedback-based method for digital pathology.
To enhance the performance of classification algorithms using pathologist feedback.
To develop a simple and adaptive GUI for this feedback system.

Main Methods:

Implementation of a bidirectional interaction pathway between algorithms and pathologists.
Development of an adaptive Graphical User Interface (GUI) for seamless feedback.
Application of the feedback method to a Convolutional Neural Network (CNN) algorithm.

Main Results:

Significant improvement in classification performance was observed.
The 25% quantile of prediction probability scores increased from 0.48 to 0.89.
The median prediction probability score rose from 0.95 to 0.99.

Conclusions:

The proposed feedback-based method effectively enhances digital pathology algorithm performance.
Pathologist feedback is crucial for refining computational pathology tools.
The developed GUI facilitates efficient integration of human expertise into AI models.