Active Learning Strategies on a Real-World Thyroid Ultrasound Dataset

Hari Sreedhar*, Guillaume P.R. Lajoinie, Charles Raffaelli, Hervé Delingette

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review


Machine learning applications in ultrasound imaging are limited by access to ground-truth expert annotations, especially in specialized applications such as thyroid nodule evaluation. Active learning strategies seek to alleviate this concern by making more effective use of expert annotations; however, many proposed techniques do not adapt well to small-scale (i.e. a few hundred images) datasets. In this work, we test active learning strategies including an uncertainty-weighted selection approach with supervised and semi-supervised learning to evaluate the effectiveness of these tools for the prediction of nodule presence on a clinical ultrasound dataset. The results on this as well as two other medical image datasets suggest that even successful active learning strategies have limited clinical significance in terms of reducing annotation burden.

Original languageEnglish
Title of host publicationData Augmentation, Labelling, and Imperfections - 3rd MICCAI Workshop, DALI 2023 Held in Conjunction with MICCAI 2023, Proceedings
EditorsYuan Xue, Chen Chen, Chao Chen, Lianrui Zuo, Yihao Liu
Number of pages10
ISBN (Print)9783031581700
Publication statusPublished - 27 Apr 2024
Event3rd International Workshop on Data Augmentation, Labeling, and Imperfections, DALI 2023 - Vancouver, Canada
Duration: 12 Oct 202312 Oct 2023
Conference number: 3

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14379 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Workshop3rd International Workshop on Data Augmentation, Labeling, and Imperfections, DALI 2023
Abbreviated titleDALI 2023
OtherIn conjunction with the 26th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2023


  • 2024 OA procedure
  • Thyroid cancer
  • Ultrasound imaging
  • Active learning


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