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Deep Neural Networks for Image-Based Dietary Assessment

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Published on: March 13, 2021

An integrated iterative annotation technique for easing neural network training in medical image analysis.

Brendon Lutnick¹, Brandon Ginley¹, Darshana Govind¹

¹Department of Pathology & Anatomical Sciences, SUNY Buffalo, New York, NY, USA.

Nature Machine Intelligence

|June 13, 2019

Summary

This study introduces a human-in-the-loop interface to improve neural network training for medical image analysis. It reduces annotation burden and enhances segmentation accuracy in pathology and radiology.

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Area of Science:

Medical Imaging
Computational Pathology
Artificial Intelligence in Medicine

Background:

Neural networks offer advanced quantitative analysis for medical fields.
Widespread adoption is hindered by complex training requirements and extensive human annotation needs.
Digital pathology and medical imaging require efficient annotation tools.

Purpose of the Study:

To develop an intuitive interface for neural network data annotation and prediction visualization in digital pathology.
To reduce the annotation burden in medical AI development using a 'human-in-the-loop' approach.
To demonstrate the adaptability of this technique across different medical imaging domains.

Main Methods:

Developed an intuitive interface integrated with a whole-slide viewer for pathology.
Implemented a 'human-in-the-loop' strategy for iterative annotation refinement.
Applied the technique to segment renal microcompartments in human and mouse pathology data.
Validated the approach by segmenting human prostate glands from radiology images.

Main Results:

Iterative human interaction with automated annotations significantly improved segmentation accuracy for renal microcompartments.
The 'human-in-the-loop' method demonstrated effectiveness in reducing the overall annotation effort.
Successful adaptation of the technique for segmenting prostate glands in radiology imaging data was achieved.

Conclusions:

The 'human-in-the-loop' interface effectively enhances neural network performance in medical image segmentation.
This approach addresses key challenges in training AI models for pathology and other medical imaging fields.
The developed tool shows promise for broader applications in quantitative medical image analysis.