Style Content Decomposition-based Data Augmentation for Domain Generalizable Medical Image Segmentation

• URL: http://arxiv.org/abs/2502.20619v1
• Date: Fri, 28 Feb 2025 00:56:46 GMT
• Title: Style Content Decomposition-based Data Augmentation for Domain Generalizable Medical Image Segmentation
• Authors: Zhiqiang Shen, Peng Cao, Jinzhu Yang, Osmar R. Zaiane, Zhaolin Chen,
• Abstract summary: We decompose an image into its style code and content map and reveal that domain shifts in medical images involve: textbfstyle shifts (emphi.e., differences in image appearance) and textbfcontent shifts (emphi.e., variations in anatomical structures)<n>We propose textbfStyCona, a textbfstyle textbfcontent decomposition-based data textbfaugmentation method that innovatively augments both image style and content
• Score: 22.69909762038458
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Due to the domain shifts between training and testing medical images, learned segmentation models often experience significant performance degradation during deployment. In this paper, we first decompose an image into its style code and content map and reveal that domain shifts in medical images involve: \textbf{style shifts} (\emph{i.e.}, differences in image appearance) and \textbf{content shifts} (\emph{i.e.}, variations in anatomical structures), the latter of which has been largely overlooked. To this end, we propose \textbf{StyCona}, a \textbf{sty}le \textbf{con}tent decomposition-based data \textbf{a}ugmentation method that innovatively augments both image style and content within the rank-one space, for domain generalizable medical image segmentation. StyCona is a simple yet effective plug-and-play module that substantially improves model generalization without requiring additional training parameters or modifications to the segmentation model architecture. Experiments on cross-sequence, cross-center, and cross-modality medical image segmentation settings with increasingly severe domain shifts, demonstrate the effectiveness of StyCona and its superiority over state-of-the-arts. The code is available at https://github.com/Senyh/StyCona.

Related papers

• ConDSeg: A General Medical Image Segmentation Framework via Contrast-Driven Feature Enhancement [5.117018155594986]
  We propose a framework called Contrast-Driven Medical Image (ConDSeg)<n>It is designed to improve the encoder's robustness in various illumination and contrast scenarios.<n>It accurately locates entities of different sizes in the image, thus avoiding erroneous learning of co-occurrence features.
  arXiv  Detail & Related papers  (2024-12-11T12:34:49Z)
• Language Guided Domain Generalized Medical Image Segmentation [68.93124785575739]
  Single source domain generalization holds promise for more reliable and consistent image segmentation across real-world clinical settings. We propose an approach that explicitly leverages textual information by incorporating a contrastive learning mechanism guided by the text encoder features. Our approach achieves favorable performance against existing methods in literature.
  arXiv  Detail & Related papers  (2024-04-01T17:48:15Z)
• MoreStyle: Relax Low-frequency Constraint of Fourier-based Image Reconstruction in Generalizable Medical Image Segmentation [53.24011398381715]
  We introduce a Plug-and-Play module for data augmentation called MoreStyle. MoreStyle diversifies image styles by relaxing low-frequency constraints in Fourier space. With the help of adversarial learning, MoreStyle pinpoints the most intricate style combinations within latent features.
  arXiv  Detail & Related papers  (2024-03-18T11:38:47Z)
• Dual-scale Enhanced and Cross-generative Consistency Learning for Semi-supervised Medical Image Segmentation [49.57907601086494]
  Medical image segmentation plays a crucial role in computer-aided diagnosis. We propose a novel Dual-scale Enhanced and Cross-generative consistency learning framework for semi-supervised medical image (DEC-Seg)
  arXiv  Detail & Related papers  (2023-12-26T12:56:31Z)
• Devil is in Channels: Contrastive Single Domain Generalization for Medical Image Segmentation [21.079667938055668]
  We propose a textbfChannel-level textbfContrastive textbfSingle textbfDomain textbfGeneralization model for medical image segmentation. Our method is novel in the contrastive perspective that enables channel-wise feature disentanglement using a single source domain.
  arXiv  Detail & Related papers  (2023-06-08T14:49:32Z)
• Implicit Anatomical Rendering for Medical Image Segmentation with Stochastic Experts [11.007092387379078]
  We propose MORSE, a generic implicit neural rendering framework designed at an anatomical level to assist learning in medical image segmentation. Our approach is to formulate medical image segmentation as a rendering problem in an end-to-end manner. Our experiments demonstrate that MORSE can work well with different medical segmentation backbones.
  arXiv  Detail & Related papers  (2023-04-06T16:44:03Z)
• BayeSeg: Bayesian Modeling for Medical Image Segmentation with Interpretable Generalizability [15.410162313242958]
  We propose an interpretable Bayesian framework (BayeSeg) to enhance model generalizability for medical image segmentation. Specifically, we first decompose an image into a spatial-correlated variable and a spatial-variant variable, assigning hierarchical Bayesian priors to explicitly force them to model the domain-stable shape and domain-specific appearance information respectively. Finally, we develop a variational Bayesian framework to infer the posterior distributions of these explainable variables.
  arXiv  Detail & Related papers  (2023-03-03T04:48:37Z)
• Self-Supervised Correction Learning for Semi-Supervised Biomedical Image Segmentation [84.58210297703714]
  We propose a self-supervised correction learning paradigm for semi-supervised biomedical image segmentation. We design a dual-task network, including a shared encoder and two independent decoders for segmentation and lesion region inpainting. Experiments on three medical image segmentation datasets for different tasks demonstrate the outstanding performance of our method.
  arXiv  Detail & Related papers  (2023-01-12T08:19:46Z)
• Integrating Visuospatial, Linguistic and Commonsense Structure into Story Visualization [81.26077816854449]
  We first explore the use of constituency parse trees for encoding structured input. Second, we augment the structured input with commonsense information and study the impact of this external knowledge on the generation of visual story. Third, we incorporate visual structure via bounding boxes and dense captioning to provide feedback about the characters/objects in generated images.
  arXiv  Detail & Related papers  (2021-10-21T00:16:02Z)
• Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalization [112.68171734288237]
  We propose a novel framework for discriminative pixel-level tasks using a generative model of both images and labels. We learn a generative adversarial network that captures the joint image-label distribution and is trained efficiently using a large set of unlabeled images. We demonstrate strong in-domain performance compared to several baselines, and are the first to showcase extreme out-of-domain generalization.
  arXiv  Detail & Related papers  (2021-04-12T21:41:25Z)
• Image Translation by Latent Union of Subspaces for Cross-Domain Plaque Detection [6.114454943178102]
  Calcified plaque in the aorta and pelvic arteries is associated with coronary artery calcification and is a strong predictor of heart attack. Current calcified plaque detection models show poor generalizability to different domains. We propose an image translation network using a shared union of subspaces constraint.
  arXiv  Detail & Related papers  (2020-05-22T20:35:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.