Revisiting Data Scaling Law for Medical Segmentation

• URL: http://arxiv.org/abs/2511.13883v1
• Date: Mon, 17 Nov 2025 20:09:12 GMT
• Title: Revisiting Data Scaling Law for Medical Segmentation
• Abstract summary: This study focuses on the scaling relationship with data size in the context of medical anatomical segmentation.<n>We analyze scaling laws for anatomical segmentation across 15 semantic tasks and 4 imaging modalities.<n>Motivated by the topological isomorphism in images sharing anatomical structures, we evaluate the impact of deformation-guided augmentation strategies.
• Score: 17.135854290232114
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The population loss of trained deep neural networks often exhibits power law scaling with the size of the training dataset, guiding significant performance advancements in deep learning applications. In this study, we focus on the scaling relationship with data size in the context of medical anatomical segmentation, a domain that remains underexplored. We analyze scaling laws for anatomical segmentation across 15 semantic tasks and 4 imaging modalities, demonstrating that larger datasets significantly improve segmentation performance, following similar scaling trends. Motivated by the topological isomorphism in images sharing anatomical structures, we evaluate the impact of deformation-guided augmentation strategies on data scaling laws, specifically random elastic deformation and registration-guided deformation. We also propose a novel, scalable image augmentation approach that generates diffeomorphic mappings from geodesic subspace based on image registration to introduce realistic deformation. Our experimental results demonstrate that both registered and generated deformation-based augmentation considerably enhance data utilization efficiency. The proposed generated deformation method notably achieves superior performance and accelerated convergence, surpassing standard power law scaling trends without requiring additional data. Overall, this work provides insights into the understanding of segmentation scalability and topological variation impact in medical imaging, thereby leading to more efficient model development with reduced annotation and computational costs.

Related papers

• Data-Efficient Meningioma Segmentation via Implicit Spatiotemporal Mixing and Sim2Real Semantic Injection [6.992254817538211]
  We propose a novel dual-augmentation framework that integrates spatial manifold expansion and semantic object injection.<n>We show that our framework significantly enhances the data efficiency and robustness of state-of-the-art models, including nnU-Net and U-Mamba.
  arXiv  Detail & Related papers  (2026-01-19T09:11:28Z)
• Granular-ball Guided Masking: Structure-aware Data Augmentation [97.18560547134587]
  Granular-ball Guided Masking (GBGM) is a structure-aware augmentation strategy guided by Granular-ball Computing (GBC)<n>GBGM adaptively preserves semantically rich, structurally important regions while suppressing redundant areas through a coarse-to-fine hierarchical masking process.<n>Experiments on multiple benchmarks demonstrate consistent improvements in classification accuracy and masked image reconstruction.
  arXiv  Detail & Related papers  (2025-12-24T07:15:33Z)
• High-Fidelity Scientific Simulation Surrogates via Adaptive Implicit Neural Representations [51.90920900332569]
  Implicit neural representations (INRs) offer a compact and continuous framework for modeling spatially structured data.<n>Recent approaches address this by introducing additional features along rigid geometric structures.<n>We propose a simple yet effective alternative: Feature-Adaptive INR (FA-INR)
  arXiv  Detail & Related papers  (2025-06-07T16:45:17Z)
• Boosting Medical Image Segmentation Performance with Adaptive Convolution Layer [6.887244952811574]
  We propose an adaptive layer placed ahead of leading deep-learning models such as UCTransNet. Our approach enhances the network's ability to handle diverse anatomical structures and subtle image details. It consistently outperforms traditional CNNs with fixed kernel sizes with a similar number of parameters.
  arXiv  Detail & Related papers  (2024-04-17T13:18:39Z)
• Learning-Based Biharmonic Augmentation for Point Cloud Classification [79.13962913099378]
  Biharmonic Augmentation (BA) is a novel and efficient data augmentation technique. BA diversifies point cloud data by imposing smooth non-rigid deformations on existing 3D structures. We present AdvTune, an advanced online augmentation system that integrates adversarial training.
  arXiv  Detail & Related papers  (2023-11-10T14:04:49Z)
• Self-supervised Semantic Segmentation: Consistency over Transformation [3.485615723221064]
  We propose a novel self-supervised algorithm, textbfS$3$-Net, which integrates a robust framework based on the proposed Inception Large Kernel Attention (I-LKA) modules. We leverage deformable convolution as an integral component to effectively capture and delineate lesion deformations for superior object boundary definition. Our experimental results on skin lesion and lung organ segmentation tasks show the superior performance of our method compared to the SOTA approaches.
  arXiv  Detail & Related papers  (2023-08-31T21:28:46Z)
• ARHNet: Adaptive Region Harmonization for Lesion-aware Augmentation to Improve Segmentation Performance [61.04246102067351]
  We propose a foreground harmonization framework (ARHNet) to tackle intensity disparities and make synthetic images look more realistic. We demonstrate the efficacy of our method in improving the segmentation performance using real and synthetic images.
  arXiv  Detail & Related papers  (2023-07-02T10:39:29Z)
• Local Magnification for Data and Feature Augmentation [53.04028225837681]
  We propose an easy-to-implement and model-free data augmentation method called Local Magnification (LOMA) LOMA generates additional training data by randomly magnifying a local area of the image. Experiments show that our proposed LOMA, though straightforward, can be combined with standard data augmentation to significantly improve the performance on image classification and object detection.
  arXiv  Detail & Related papers  (2022-11-15T02:51:59Z)
• Segmentation Consistency Training: Out-of-Distribution Generalization for Medical Image Segmentation [2.0978389798793873]
  Generalizability is seen as one of the major challenges in deep learning, in particular in the domain of medical imaging. We introduce Consistency Training, a training procedure and alternative to data augmentation. We demonstrate that Consistency Training outperforms conventional data augmentation on several out-of-distribution datasets.
  arXiv  Detail & Related papers  (2022-05-30T20:57:15Z)
• Enhancing MR Image Segmentation with Realistic Adversarial Data Augmentation [17.539828821476224]
  We propose an adversarial data augmentation approach to improve the efficiency in utilizing training data. We present a generic task-driven learning framework, which jointly optimize a data augmentation model and a segmentation network during training. The proposed adversarial data augmentation does not rely on generative networks and can be used as a plug-in module in general segmentation networks.
  arXiv  Detail & Related papers  (2021-08-07T11:32:37Z)
• Few-shot Medical Image Segmentation using a Global Correlation Network with Discriminative Embedding [60.89561661441736]
  We propose a novel method for few-shot medical image segmentation. We construct our few-shot image segmentor using a deep convolutional network trained episodically. We enhance discriminability of deep embedding to encourage clustering of the feature domains of the same class.
  arXiv  Detail & Related papers  (2020-12-10T04:01:07Z)
• Automatic Data Augmentation for 3D Medical Image Segmentation [37.262350163905445]
  It is the first time that differentiable automatic data augmentation is employed in medical image segmentation tasks. Our numerical experiments demonstrate that the proposed approach significantly outperforms existing build-in data augmentation of state-of-the-art models.
  arXiv  Detail & Related papers  (2020-10-07T12:51:17Z)
• 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.