Abstract
Collecting pixel-level labels for medical datasets can be a laborious and expensive process, and enhancing segmentation performance with a scarcity of labeled data is a crucial challenge. This work introduces AugPaint, a data augmentation framework that utilizes inpainting to generate image-label pairs from limited labeled data. AugPaint leverages latent diffusion models, known for their ability to generate high-quality in-domain images with low overhead, and adapts the sampling process for the inpainting task without need for retraining. Specifically, given a pair of image and label mask, we crop the area labeled with the foreground and condition on it during reversed denoising process for every noise level. Masked background area would gradually be filled in, and all generated images are paired with the label mask. This approach ensures the accuracy of match between synthetic images and label masks, setting it apart from existing dataset generation methods. The generated images serve as valuable supervision for training downstream segmentation models, effectively addressing the challenge of limited annotations. We conducted extensive evaluations of our data augmentation method on four public medical image segmentation datasets, including CT, MRI, and skin imaging. Results across all datasets demonstrate that AugPaint outperforms state-of-the-art label-efficient methodologies, significantly improving segmentation performance.
Abstract (translated)
为医疗数据集收集像素级别的标签可能是一个繁琐且昂贵的过程，而在标记数据稀缺的情况下提高分割性能是一项关键挑战。这项工作引入了一种名为AugPaint的数据增强框架，该框架利用图像修复技术从有限的标注数据中生成图像-标签对。AugPaint采用潜在扩散模型，这种模型因其能够以低计算开销生成高质量、领域内的图像而著称，并且无需重新训练即可适应图像修复任务中的采样过程。 具体而言，给定一个包含图像和标签掩码的数据对，我们裁剪出被标记为前景的区域，并在反向去噪过程中针对每个噪声水平进行条件化处理。背景遮罩下的区域将逐渐被填充完成，所有生成的图像都会与标签掩码配对。这种做法确保了合成图像与标签掩模之间匹配的准确性，从而区别于现有的数据集生成方法。 通过这种方式生成的图像可以作为有价值的监督信号用于下游分割模型的训练，有效地解决了标注不足的问题。我们在四个公开的医疗图像分割数据集中进行了广泛的数据增强方法评估，包括CT、MRI和皮肤成像。在所有数据集上的结果表明，AugPaint优于当前最先进的标记效率方法，在所有情况下都显著提升了分割性能。
URL
https://arxiv.org/abs/2506.23038
