PCaPaint：通过缓解捷径学习实现前列腺癌图像修复
PCaPaint: Prostate Cancer Inpainting by Mitigating Shortcut Learning
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中文总结 AI 辅助
针对前列腺癌MRI合成图像修复中的捷径学习问题，提出PCaPaint方法，采用高斯噪声填充条件、病灶区域误差训练目标及多序列潜在设计，显著提升下游分割、分类与检测性能。
中文摘要 AI 辅助
面向肿瘤特定应用的AI系统开发受到标注数据稀缺的限制。合成肿瘤图像修复提供了一种有前景的方法，但对于包含高分辨率多序列数据的前列腺癌MRI而言，该方法面临挑战。尽管利用潜在扩散模型（LDMs）的方法能够实现大规模合成，但它们容易陷入捷径学习，即简单地复现通过掩膜病灶区域创建的条件图像。在本工作中，我们提出了PCaPaint，一种基于LDM的前列腺癌图像修复方法，该方法明确解决了这一失败模式。为了克服损害合成肿瘤纹理的捷径学习，我们提出了一种简单而高效的条件策略，即用高斯噪声填充条件图像，并提供了理论依据。此外，我们为LDM提出了一种新的训练目标，该目标强调病灶区域内的误差。进一步地，我们引入了一种多序列潜在设计，其中T2w扫描和DWI&ADC扫描分别使用两个独立的自动编码器进行压缩，以保留它们各自独特的频率特征。大量实验表明，生成的合成数据提升了前列腺病灶分割、患者级分类和病灶级检测的下游性能。此外，我们的方法在下游性能和合成图像质量方面均显著优于近期基于LDM的最先进的肿瘤图像修复方法。
英文摘要
The development of AI systems for tumor-specific applications is limited by the scarcity of labeled data. Synthetic tumor inpainting offers a promising approach but faces challenges for prostate cancer MRI which contains high-resolution multi-sequence data. Although methods leveraging latent diffusion models (LDMs) enable large-volume synthesis, they are prone to shortcut learning, simply reproducing the condition image created by masking the lesion region. In this work, we introduce PCaPaint, a prostate cancer inpainting method based on LDMs that explicitly addresses this failure mode. To overcome shortcut learning that compromises synthetic tumor texture, we propose a simple yet efficient conditioning strategy in which the condition image is filled with Gaussian noise, and we provide theoretical justification. In addition, we propose a novel training objective for LDM that emphasizes the error within the lesion region. Furthermore, we introduce a multi-sequence latent design, in which T2w scans and DWI&ADC scans are compressed using two separate autoencoders to preserve their distinct frequency characteristics. Extensive experiments demonstrate that the generated synthetic data improves downstream performance in prostate lesion segmentation, patient-level classification and lesion-level detection. Furthermore, our method significantly outperforms a recent state-of-the-art LDM-based tumor inpainting method both in downstream performance and in synthetic image quality.
发表机构
- GE HealthCare（GE医疗）
机构由 AI 辅助整理，请以论文原文为准。
