bibtext: @InProceedings{ Su_SelfPaced_MICCAI2024,
                   author = { Su, Junming and Shen, Zhiqiang and Cao, Peng and Yang, Jinzhu and Zaiane, Osmar R. },
                   title = { { Self-Paced Sample Selection for Barely-Supervised Medical Image Segmentation } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15009 },
                   month = {October},
                   pages = { pending },
                }
abstract: The existing barely-supervised medical image segmentation (BSS) methods, adopting a registration-segmentation paradigm, aim to learn from data with very few annotations to mitigate the extreme label scarcity problem.
  However, this paradigm poses a challenge: pseudo-labels generated by image registration come with significant noise. 
  To address this issue, we propose a self-paced sample selection framework (SPSS) for BSS. 
  Specifically, SPSS comprises two main components: 1) self-paced uncertainty sample selection (SU) for explicitly improving the quality of pseudo labels in the image space, and 2) self-paced bidirectional feature contrastive learning (SC) for implicitly improving the quality of pseudo labels through enhancing the separability between class semantics in the feature space. 
  Both SU and SC are trained collaboratively in a self-paced learning manner, ensuring that SPSS can learn from high-quality pseudo labels for BSS. 
  Extensive experiments on two public medical image segmentation datasets demonstrate the effectiveness and superiority of SPSS over the state-of-the-art.
title: Self-Paced Sample Selection for Barely-Supervised Medical Image Segmentation
authors: Su, Junming Shen, Zhiqiang Cao, Peng Yang, Jinzhu Zaiane, Osmar R.