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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Trustworthy Medical Image Segmentation with improved performance for in-distribution samples.

Sneha Shukla¹, Lokendra Birla², Anup Kumar Gupta¹

¹Indian Institute of Technology Indore, Indore, India.

Neural Networks : the Official Journal of the International Neural Network Society

|July 24, 2023

Summary

This study introduces TrustMIS, a new method to improve the trustworthiness of Deep Learning models in medical image segmentation. TrustMIS enhances predictions from in-distribution samples, addressing critical issues in medical AI.

Keywords:

Confidence measure Deep Learning In-Distribution samples Medical Image Segmentation Trustworthiness

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Area of Science:

Artificial Intelligence
Medical Imaging
Computer Vision

Background:

Deep Learning (DL) models achieve high performance in Medical Image Segmentation (MIS) but suffer from non-transparent predictions.
Erroneous In-Distribution (ID) and Out-Of-Distribution (OOD) samples lead to distrust and potential harm in medical applications.
Existing methods focus on OOD detection, but trustworthiness of ID samples in MIS remains a significant challenge.

Purpose of the Study:

To propose a novel method, TrustMIS (Trustworthy Medical Image Segmentation), to enhance the trustworthiness and performance of DL-based MIS models for ID samples.
To address the critical need for reliable predictions in medical image analysis.
To provide a quantitative measure of trustworthiness for MIS models.

Main Methods:

TrustMIS employs a three-fold approach: Investigating Trustworthiness (IT), Improving Non-Trustworthy prediction (INT), and Classifier Switching Operation (CSO).
The IT method analyzes input characteristics and consistency to assess trustworthiness.
The INT method refines segmentation by leveraging input transformations (e.g., rotation), and CSO selects the most trustworthy model predictions.

Main Results:

TrustMIS successfully provides a trustworthiness measure for DL-based MIS models.
The method demonstrates improved performance on state-of-the-art MIS models using publicly available datasets.
Experimental results show TrustMIS outperforms existing methods in enhancing trustworthiness and segmentation accuracy.

Conclusions:

TrustMIS offers a robust solution for improving the reliability of Deep Learning models in Medical Image Segmentation.
The proposed method enhances the trustworthiness of predictions from in-distribution samples, a crucial step for clinical adoption.
The availability of the implementation facilitates further research and application of trustworthy AI in medical imaging.