Documentdetail
ID kaart
oai:HAL:hal-04209622v1
Onderwerp
Thyroid cancer Active learning Ultrasound imaging [INFO.INFO-IM]Computer Science [cs...Auteur
Sreedhar, Hari Lajoinie, Guillaume, P R Raffaelli, Charles Delingette, HervéLangue
enEditor
HAL CCSD
Categorie
INRIA - Institut National de Recherche en Informatique et en Automatique
Jaar
2023
vermelding datum
05-12-2023
Trefwoorden
thyroid imaging ultrasound active learning strategiesBeschrijving
International audience; 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.
Sreedhar, Hari,Lajoinie, Guillaume, P R,Raffaelli, Charles,Delingette, Hervé, 2023, Active Learning Strategies on a Real-World Thyroid Ultrasound Dataset, HAL CCSD
Analysis of the ICML 2023 Ranking Data: Can Authors' Opinions of Their Own Papers Assist Peer Review in Machine Learning?
peer author-provided analysis rankings scores review learning machine science computer
