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Updated: Jan 12, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Class-incremental learning using push-pull autoencoder for chest X-ray diagnosis.

Jayant Mahawar¹, Angshuman Paul¹

¹Department of Computer Science and Engineering, Indian Institute of Technology Jodhpur, N.H. 62, Nagaur Road, Karwar, Jodhpur, 342030, Rajasthan, India.

Computers in Biology and Medicine

|November 6, 2025

Summary

This summary is machine-generated.

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Class-incremental learning (CIL) models for chest X-ray diagnosis were improved using the Push-Pull Autoencoder (PPAE). PPAE reduces catastrophic forgetting, enhancing diagnostic accuracy for new and old diseases.

Area of Science:

Medical Imaging
Artificial Intelligence
Machine Learning

Background:

Class-incremental learning (CIL) enables models to learn new classes without prior data, crucial for evolving medical datasets.
Existing CIL models struggle with chest X-ray diagnosis, while deep learning models for X-rays often exhibit catastrophic forgetting in CIL settings.

Purpose of the Study:

To develop a novel CIL framework tailored for chest X-ray analysis, addressing the limitations of current models.
To mitigate catastrophic forgetting and improve diagnostic performance in incremental learning scenarios for medical imaging.

Main Methods:

Proposed the Push-Pull Autoencoder (PPAE), a model utilizing a dual latent space to disentangle abnormality-specific and abnormality-agnostic features.
Implemented a coreset generation algorithm to select representative exemplars, preserving knowledge from previous classes without full retraining.

Keywords:

Chest X-ray diagnosis Class-incremental learning Contrastive learning Coreset Push-pull autoencoder

Related Experiment Videos

Last Updated: Jan 12, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Trained PPAE to refine feature representations by clustering similar samples and distinguishing dissimilar ones based on learned features.

Main Results:

Achieved up to 3% improvement in F1 score and 4% improvement in AUROC across diverse chest X-ray datasets.
Demonstrated significant reduction in catastrophic forgetting, maintaining high diagnostic accuracy on previously learned classes.
Validated the framework's robustness and effectiveness in continuous learning tasks for chest X-ray diagnosis.

Conclusions:

The PPAE framework effectively addresses catastrophic forgetting in class-incremental learning for chest X-ray diagnosis.
The proposed method enhances continuous learning capabilities, offering a promising approach for evolving medical diagnostic systems.
PPAE shows potential for advancing long-term, adaptive diagnostic solutions in medical imaging.