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Updated: Jul 8, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Representative Data Selection for Efficient Medical Incremental Learning.

Bo-Quan Wei, Jen-Jee Chen, Yu-Chee Tseng

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|December 12, 2023

Summary

This study introduces a novel data selection method using a variational autoencoder (VAE) and adversarial network for efficient incremental learning. It enables continuous model improvement with new data, crucial for medical imaging and defect detection.

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Area of Science:

Artificial Intelligence
Machine Learning
Computer Vision

Background:

Deep neural network training requires substantial annotated data, which is often challenging to acquire at once in fields like medical imaging and industrial defect detection.
New data frequently arrives incrementally, necessitating models that can adapt and improve over time without complete retraining.

Purpose of the Study:

To develop a data selection strategy for efficient incremental learning, enabling models to retrain with a fixed amount of data as new information becomes available.
To enable continuous model improvement and adaptation to new data while preserving previously learned information.

Main Methods:

A hybrid approach combining a variational autoencoder (VAE) with an adversarial network for intelligent data selection.
Constraining retraining to a fixed subset of data to achieve fast model updates.
Validation on the LGG Segmentation dataset for a semantic segmentation task.

Main Results:

The VAE-based data selection model combined with adversarial training effectively selects representative and reliable data subsets.
Achieved time-efficient incremental learning, allowing for rapid model retraining and adaptation.
Demonstrated the model's ability to continually learn from small, incoming data batches without catastrophic forgetting.

Conclusions:

The proposed framework offers a practical solution for incremental learning in data-scarce scenarios, particularly in medical image analysis.
Enables immediate visualization of model improvements as new annotated data becomes available, facilitating quicker clinical relevance.
Facilitates efficient adaptation of deep learning models to evolving datasets through smart data selection and retraining.