HSMix: Hard and Soft Mixing Data Augmentation for Medical Image Segmentation

• URL: http://arxiv.org/abs/2511.17614v1
• Date: Tue, 18 Nov 2025 11:49:22 GMT
• Title: HSMix: Hard and Soft Mixing Data Augmentation for Medical Image Segmentation
• Authors: Danyang Sun, Fadi Dornaika, Nagore Barrena,
• Abstract summary: We propose HSMix, a novel approach to local image editing data augmentation involving hard and soft mixing.<n>Our method fully exploits both the prior contour and saliency information, thus preserving local semantic information in the augmented images.<n>Our method is a plug-and-play solution that is model agnostic and applicable to a range of medical imaging modalities.
• Score: 11.212631557877971
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Due to the high cost of annotation or the rarity of some diseases, medical image segmentation is often limited by data scarcity and the resulting overfitting problem. Self-supervised learning and semi-supervised learning can mitigate the data scarcity challenge to some extent. However, both of these paradigms are complex and require either hand-crafted pretexts or well-defined pseudo-labels. In contrast, data augmentation represents a relatively simple and straightforward approach to addressing data scarcity issues. It has led to significant improvements in image recognition tasks. However, the effectiveness of local image editing augmentation techniques in the context of segmentation has been less explored. We propose HSMix, a novel approach to local image editing data augmentation involving hard and soft mixing for medical semantic segmentation. In our approach, a hard-augmented image is created by combining homogeneous regions (superpixels) from two source images. A soft mixing method further adjusts the brightness of these composed regions with brightness mixing based on locally aggregated pixel-wise saliency coefficients. The ground-truth segmentation masks of the two source images undergo the same mixing operations to generate the associated masks for the augmented images. Our method fully exploits both the prior contour and saliency information, thus preserving local semantic information in the augmented images while enriching the augmentation space with more diversity. Our method is a plug-and-play solution that is model agnostic and applicable to a range of medical imaging modalities. Extensive experimental evidence has demonstrated its effectiveness in a variety of medical segmentation tasks. The source code is available in https://github.com/DanielaPlusPlus/HSMix.

Related papers

• M$^3$HL: Mutual Mask Mix with High-Low Level Feature Consistency for Semi-Supervised Medical Image Segmentation [10.42922059959177]
  We propose a novel method called Mutual Mask Mix with High-Low level feature consistency (M$3$HL) to address the aforementioned challenges.<n>Our method achieves state-of-the-art performance on widely adopted medical image segmentation benchmarks including the ACDC and LA datasets.
  arXiv  Detail & Related papers  (2025-08-04T05:42:10Z)
• Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation [8.772764547425291]
  AugPaint is a framework that generates image-label pairs from limited labeled data.<n>We conducted evaluations of our data augmentation method on four public medical image segmentation datasets.<n>Results across all datasets demonstrate that AugPaint outperforms state-of-the-art label-efficient methodologies.
  arXiv  Detail & Related papers  (2025-06-28T23:44:18Z)
• DiffuseMix: Label-Preserving Data Augmentation with Diffusion Models [18.44432223381586]
  Recently, a number of image-mixing-based augmentation techniques have been introduced to improve the generalization of deep neural networks. In these techniques, two or more randomly selected natural images are mixed together to generate an augmented image. We propose DiffuseMix, a novel data augmentation technique that leverages a diffusion model to reshape training images.
  arXiv  Detail & Related papers  (2024-04-05T05:31:02Z)
• 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)
• Multi-Level Global Context Cross Consistency Model for Semi-Supervised Ultrasound Image Segmentation with Diffusion Model [0.0]
  We propose a framework that uses images generated by a Latent Diffusion Model (LDM) as unlabeled images for semi-supervised learning. Our approach enables the effective transfer of probability distribution knowledge to the segmentation network, resulting in improved segmentation accuracy.
  arXiv  Detail & Related papers  (2023-05-16T14:08:24Z)
• 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)
• Cross-level Contrastive Learning and Consistency Constraint for Semi-supervised Medical Image Segmentation [46.678279106837294]
  We propose a cross-level constrastive learning scheme to enhance representation capacity for local features in semi-supervised medical image segmentation. With the help of the cross-level contrastive learning and consistency constraint, the unlabelled data can be effectively explored to improve segmentation performance.
  arXiv  Detail & Related papers  (2022-02-08T15:12:11Z)
• Positional Contrastive Learning for Volumetric Medical Image Segmentation [13.086140606803408]
  We propose a novel positional contrastive learning framework to generate contrastive data pairs. The proposed PCL method can substantially improve the segmentation performance compared to existing methods in both semi-supervised setting and transfer learning setting.
  arXiv  Detail & Related papers  (2021-06-16T22:15:28Z)
• ResizeMix: Mixing Data with Preserved Object Information and True Labels [57.00554495298033]
  We study the importance of the saliency information for mixing data, and find that the saliency information is not so necessary for promoting the augmentation performance. We propose a more effective but very easily implemented method, namely ResizeMix.
  arXiv  Detail & Related papers  (2020-12-21T03:43:13Z)
• SnapMix: Semantically Proportional Mixing for Augmenting Fine-grained Data [124.95585891086894]
  Proposal is called Semantically Proportional Mixing (SnapMix) It exploits class activation map (CAM) to lessen the label noise in augmenting fine-grained data. Our method consistently outperforms existing mixed-based approaches.
  arXiv  Detail & Related papers  (2020-12-09T03:37:30Z)
• Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation [84.7571086566595]
  We propose improvements over previous GAN-based medical image synthesis methods by jointly encoding the intrinsic relationship of geometry and shape. The proposed method outperforms state-of-the-art segmentation methods on the public RETOUCH dataset having images captured from different acquisition procedures.
  arXiv  Detail & Related papers  (2020-03-31T11:50:43Z)

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.