Exploring Minimally Sufficient Representation in Active Learning through Label-Irrelevant Patch Augmentation

Zhiyu Xue, Yinlong Dai, Qi Lei

Research output: Contribution to journalConference articlepeer-review


Deep learning models, which require abundant labeled data for training, are expensive and time-consuming to implement, particularly in medical imaging. Active learning (AL) aims to maximize model performance with few labeled samples by gradually expanding and labeling a new training set. In this work, we intend to learn a "good" feature representation that is both sufficient and minimal, facilitating effective AL for medical image classification. This work proposes an efficient AL framework based on off-the-shelf self-supervised learning models, complemented by a label-irrelevant patch augmentation scheme. This scheme is designed to reduce redundancy in the learned features and mitigate overfitting in the progress of AL. Our framework offers efficiency to AL in terms of parameters, samples, and computational costs. The benefits of this approach are extensively validated across various medical image classification tasks employing different AL strategies.

Original languageEnglish (US)
Pages (from-to)419-439
Number of pages21
JournalProceedings of Machine Learning Research
StatePublished - 2024
Event1st Conference on Parsimony and Learning, CPAL 2024 - Hongkong, China
Duration: Jan 3 2024Jan 6 2024

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering
  • Statistics and Probability


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