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Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

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Deep Quantile Regression for Uncertainty Estimation in Unsupervised and Supervised Lesion Detection.

Haleh Akrami¹, Anand A Joshi², Sergül Aydöre³

¹Department of Biomedical Engineering, University of Southern California, Los Angeles, USA.

The Journal of Machine Learning for Biomedical Imaging

|January 30, 2023

Summary

This summary is machine-generated.

This study introduces quantile regression to quantify uncertainty in deep learning for lesion detection. The novel methods, QR-VAE and BQR, improve accuracy in both unsupervised and supervised settings, crucial for medical diagnosis.

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Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

Area of Science:

Machine Learning
Medical Image Analysis
Deep Learning

Background:

Deep learning models can be over-confident, especially with limited data.
Quantifying uncertainty is critical for medical applications like lesion detection and diagnosis.
Existing Variational AutoEncoder (VAE) methods suffer from variance shrinkage.

Purpose of the Study:

To propose quantile regression for quantifying aleatoric uncertainty in lesion detection.
To develop unsupervised (QR-VAE) and supervised (BQR) methods for uncertainty quantification.
To improve the reliability of deep learning in critical medical applications.

Main Methods:

Quantile regression applied to unsupervised lesion detection using a Variational AutoEncoder (VAE).
Developed Quantile-Regression VAE (QR-VAE) to avoid variance shrinkage by directly estimating conditional quantiles.
Developed Binary Quantile Regression (BQR) for supervised lesion segmentation.

Main Results:

QR-VAE effectively quantifies uncertainty in VAE reconstructions for outlier detection.
BQR captures uncertainty in lesion boundaries, reflecting expert disagreement.
Both methods enhance lesion detection and segmentation accuracy.

Conclusions:

Quantile regression offers a robust approach to aleatoric uncertainty quantification in deep learning for medical imaging.
The proposed QR-VAE and BQR methods provide more reliable confidence estimates than standard VAEs.
These advancements are vital for trustworthy clinical diagnosis and treatment planning.