用于全切片图像生存分析的端到端多源视觉提示调优
End-to-end Multi-source Visual Prompt Tuning for Survival Analysis in Whole Slide Images
AI总结：
提出端到端多源视觉提示调优框架 VPTSurv，通过冻结编码器并仅训练视觉提示、轻量适配器与解码器优化生存预测，在两个免疫组化数据集上显著提升 C-index。
摘要：
利用病理图像进行生存分析面临相当大的挑战，因为它需要从全切片图像（WSIs）内的大量图块中定位相关信息。当前方法通常采用两阶段方式：先由预训练网络从图块中提取特征，再由生存模型使用这些特征。然而，这一过程并未以端到端方式优化生存模型，而且预提取的特征可能并不理想地适合生存预测。为解决这一局限，我们提出了一种新颖的用于生存分析的端到端视觉提示调优框架，名为 VPTSurv。VPTSurv 通过高效的编码器-解码器框架精炼特征嵌入。编码器保持固定，同时该框架引入可调节的视觉提示和适配器，从而仅通过优化轻量级适配器和解码器，即可实现专门针对生存预测的端到端训练。此外，通用的 VPTSurv 框架可将多源信息作为提示纳入，从而丰富生存模型。VPTSurv 在两个免疫组织化学病理图像数据集上使 C-index 分别大幅提升 8.7% 和 12.5%。这些显著改进凸显了端到端 VPT 框架相较于传统两阶段方法的变革性潜力。
英文摘要：
Survival analysis using pathology images poses a considerable challenge, as it requires the localization of relevant information from the multitude of tiles within whole slide images (WSIs). Current methods typically resort to a two-stage approach, where a pre-trained network extracts features from tiles, which are then used by survival models. This process, however, does not optimize the survival models in an end-to-end manner, and the pre-extracted features may not be ideally suited for survival prediction. To address this limitation, we present a novel end-to-end Visual Prompt Tuning framework for survival analysis, named VPTSurv. VPTSurv refines feature embeddings through an efficient encoder-decoder framework. The encoder remains fixed while the framework introduces tunable visual prompts and adaptors, thus permitting end-to-end training specifically for survival prediction by optimizing only the lightweight adaptors and the decoder. Moreover, the versatile VPTSurv framework accommodates multi-source information as prompts, thereby enriching the survival model. VPTSurv achieves substantial increases of 8.7% and 12.5% in the C-index on two immunohistochemical pathology image datasets. These significant improvements highlight the transformative potential of the end-to-end VPT framework over traditional two-stage methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
