bibtext: @InProceedings{ Yi_Hallucinated_MICCAI2024,
                   author = { Yi, Jingjun and Bi, Qi and Zheng, Hao and Zhan, Haolan and Ji, Wei and Huang, Yawen and Li, Shaoxin and Li, Yuexiang and Zheng, Yefeng and Huang, Feiyue },
                   title = { { Hallucinated Style Distillation for Single Domain Generalization in Medical Image Segmentation } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15010 },
                   month = {October},
                   pages = { pending },
                }
abstract: Single domain generalization (single-DG) for medical image segmentation aims to learn a style-invariant representation, which can be generalized to a variety unseen target domains, with the data from a single source. However, due to the limitation of sample diversity in the single source domain, the robustness of generalized features yielded by existing single-DG methods is still unsatisfactory.
  In this paper, we propose a novel single-DG framework, namely Hallucinated Style Distillation (HSD), to generate the robust style-invariant feature representation.
  Particularly, our HSD firstly expands the style diversity of the single source domain via hallucinating the samples with random styles.
  Then, a hallucinated cross-domain distillation paradigm is proposed to distillate the style-invariant knowledge between the original and style-hallucinated medical images.
  Since the hallucinated styles close to the source domain may over-fit our distillation paradigm, we further propose a learning objective to diversify style-invariant representation, which alleviates the over-fitting issue and smooths the learning process of generalized features.
  Extensive experiments on two standard domain generalized medical image segmentation datasets show the state-of-the-art performance of our HSD.
  Source code will be publicly available.
title: Hallucinated Style Distillation for Single Domain Generalization in Medical Image Segmentation
authors: Yi, Jingjun Bi, Qi Zheng, Hao Zhan, Haolan Ji, Wei Huang, Yawen Li, Shaoxin Li, Yuexiang Zheng, Yefeng Huang, Feiyue