PLST: A Pseudo-labels with a Smooth Transition Strategy for Medical Site Adaptation

Tomer Bar Natan, Hayit Greenspan, Jacob Goldberger

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


This study addresses the challenge of medical image segmentation when transferring a pre-trained model from one medical site to another without access to pre-existing labels. The method involves utilizing a self-training approach by generating pseudo-labels of the target domain data. To do so, a strategy that is based on a smooth transition between domains is implemented where we initially feed easy examples to the network and gradually increase the difficulty of the examples. To identify the level of difficulty, we use a binary classifier trained to distinguish between the two domains by considering that target images easier if they are classified as source examples. We demonstrate the improved performance of our method on a range of medical MRI image segmentation tasks. When integrating our approach as a post-processing step in several standard Unsupervised Domain Adaptation (UDA) algorithms, we consistently observed significant improvements in the segmentation results on test images from the target site.

Original languageEnglish
Title of host publicationDomain Adaptation and Representation Transfer - 5th MICCAI Workshop, DART 2023, Held in Conjunction with MICCAI 2023, Proceedings
EditorsLisa Koch, M. Jorge Cardoso, Enzo Ferrante, Konstantinos Kamnitsas, Mobarakol Islam, Meirui Jiang, Nicola Rieke, Sotirios A. Tsaftaris, Dong Yang
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages10
ISBN (Print)9783031458569
StatePublished - 2024
Event5th MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2023 - Vancouver, Canada
Duration: 12 Oct 202312 Oct 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14293 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference5th MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2023

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.


  • domain adaptation
  • domain shift
  • pseudo-labels
  • self-training


Dive into the research topics of 'PLST: A Pseudo-labels with a Smooth Transition Strategy for Medical Site Adaptation'. Together they form a unique fingerprint.

Cite this