用元学习训练隐式网络，仅靠像素拟合自动分割医学图像
Fit Pixels, Get Labels: Meta-learned Implicit Networks for Image Segmentation
- 用隐式神经网络同时预测像素值和类别标签
- 2D/3D脑部MRI分割任务上达U-Net水平，参数量少90%
- 适合需要轻量化、快速适配新图像的医疗影像场景
隐式神经表示（INRs）在学习表达性强且紧凑的信号表示方面取得了显著进展，但其不天然适用于分割等预测任务，因需在信号分布上学习语义结构。本文提出MetaSeg，一种用于医学图像分割的元学习框架。MetaSeg采用底层隐式神经网络，同时预测每个像素的强度值和类别标签。通过元学习过程，在图像与分割图数据集上寻找该INR的最优初始参数，使INR只需微调即可拟合未见测试图像的像素，并自动解码出类别标签。我们在2D和3D脑部MRI分割任务上评估了MetaSeg，结果表明其Dice分数与常用U-Net模型相当，但参数量减少了90%。MetaSeg为医学图像分割提供了新颖且可扩展的轻量级替代方案，相比传统资源密集型架构如U-Net和视觉变换器更具优势。
原文摘要 · Abstract (English)
Implicit neural representations (INRs) have achieved remarkable successes in learning expressive yet compact signal representations. However, they are not naturally amenable to predictive tasks such as segmentation, where they must learn semantic structures over a distribution of signals. In this study, we introduce MetaSeg, a meta-learning framework to train INRs for medical image segmentation. MetaSeg uses an underlying INR that simultaneously predicts per pixel intensity values and class labels. It then uses a meta-learning procedure to find optimal initial parameters for this INR over a training dataset of images and segmentation maps, such that the INR can simply be fine-tuned to fit pixels of an unseen test image, and automatically decode its class labels. We evaluated MetaSeg on 2D and 3D brain MRI segmentation tasks and report Dice scores comparable to commonly used U-Net models, but with $90\%$ fewer parameters. MetaSeg offers a fresh, scalable alternative to traditional resource-heavy architectures such as U-Nets and vision transformers for medical image segmentation. Our project is available at https://kushalvyas.github.io/metaseg.html .
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。