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Updated: Jul 1, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Expectation maximisation pseudo labels.

Moucheng Xu¹, Yukun Zhou², Chen Jin³

¹UCL Centre for Medical Image Computing (CMIC), University College London, 90 High Holborn, London, WC1V 6LJ, UK; UCL Department of Medical Physics and Biomedical Engineering, University College London, Gower Street, London, WC1E 6BT, UK; Satsuma Lab, University College Londo, 90 High Holborn, WC1V 6LJ, UK.

Medical Image Analysis

|March 1, 2024

Summary

This summary is machine-generated.

This study links pseudo-labelling to Expectation Maximisation, introducing Bayesian Pseudo Labels for improved semi-supervised medical image segmentation. This method enhances model robustness and accuracy in segmenting various anatomical structures.

Keywords:

Bayesian deep learning Expectation–maximisation Generative models Pseudo labels Robustness Segmentation Semi-supervised learning

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Area of Science:

Machine Learning
Medical Image Analysis
Computer Vision

Background:

Pseudo-labelling is a self-training technique using model predictions on unlabeled data.
Its empirical success is evident but lacks a strong theoretical foundation.
Existing methods may not fully leverage unlabeled data for robust model training.

Purpose of the Study:

To establish a theoretical link between pseudo-labelling and the Expectation Maximisation (EM) algorithm.
To introduce a generalized framework for pseudo-labelling, termed Bayesian Pseudo Labels (BPL).
To demonstrate the effectiveness of BPL in semi-supervised medical image segmentation tasks.

Main Methods:

Connecting pseudo-labelling to the EM algorithm to understand its underlying principles.
Generalizing pseudo-labelling using Bayes' theorem to derive Bayesian Pseudo Labels.
Developing a variational approach with adaptive thresholding for generating high-quality BPLs.
Applying pseudo-labelling and BPL to semi-supervised segmentation of medical images (CT, MRI).

Main Results:

Empirical successes of pseudo-labelling are explained through its connection to EM.
Bayesian Pseudo Labels offer a theoretically grounded generalization.
The proposed variational approach effectively generates high-quality pseudo-labels.
Significant improvements shown in semi-supervised segmentation of lung vessels, brain tumors, and prostate MRI.
Demonstrated enhancement of learned representation robustness using pseudo-labels.

Conclusions:

Pseudo-labelling is an empirical estimation of a more comprehensive EM-based formulation.
Bayesian Pseudo Labels provide a robust and theoretically sound extension.
The method significantly advances semi-supervised medical image segmentation accuracy and robustness.
The framework is applicable to diverse medical imaging modalities and segmentation tasks.