Work

Optimising Active Learning with a Decreasing-Budget-Based Strategy: A Medical Application Case Study

Year: 2025

Type: article

Abstract: The collection and annotation of data for supervised machine learning remain challenging and costly tasks, particularly in domains that demand expert knowledge. Depending on the application, labelling may require highly specialised professionals, significantly increasing the overall effort and expense. Active learning techniques offer a promising solution by reducing the number of annotations needed, thereby lowering costs without compromising model performance. This work proposes an active learning with a decreasing-budget-based strategy to reduce the effort required to annotate medical images. The strategy encourages data annotators to focus on initial iterations, optimise budget allocation, and ensure that the trained model achieves maximum performance with reduced effort in subsequent iterations. This strategy also improves the performance of deep learning models, which perform better with fewer images, reducing the specialists’ workload. This work also introduces three experiments that contribute to understanding the impact of the strategy in the annotation process.

Source: Applied Sciences

DOI: 10.3390/app151810195

Authors: Dibet Garcia Gonzalez, Maria Inês Leite, Luis Magalhães, Antonio Cunha

Institutions: University of Trás-os-Montes and Alto Douro, Centro ALGORITMI, University of Minho

Language: English

Topic: Machine Learning and Algorithms

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funder: Fundação para a Ciência e a Tecnologia