使用StyleGAN嵌入的人像照片进行癌症预后预测的深度学习方法
Deep Learning for Cancer Prognosis Prediction Using Portrait Photos by StyleGAN Embedding
AI总结：
首次使用深度学习结合StyleGAN2潜在空间嵌入从人像照片中预测癌症患者生存期，C-index达0.677，证明了2D面部图像的预后价值并消除了外部信息偏差。
摘要：
癌症患者的生存预测对于最佳治疗方案选择和患者管理至关重要。当前的生存预测方法通常从患者的临床记录数据或生物和成像数据中提取生存信息。在实践中，经验丰富的临床医生可以根据患者可观察到的身体外观（主要是面部特征）对患者的健康状况进行初步评估。然而，这种评估具有高度主观性。在这项工作中，首次研究了使用深度学习客观捕获和利用传统人像照片中包含的预后信息进行生存预测的有效性。在癌症患者照片的自定义数据集上对预训练的StyleGAN2模型进行微调，使其生成器具备适合患者照片的生成能力。然后使用StyleGAN2将照片嵌入到其高度表达性的潜在空间中。利用最先进的生存分析模型，并基于StyleGAN的潜在空间照片嵌入，该方法达到了0.677的C-index，显著高于随机概率，证明了简单2D面部图像中蕴含的预后价值。此外，得益于StyleGAN可解释的潜在空间，可以验证我们的生存预测模型依赖于关键面部特征，消除了来自服装或背景等外部信息的偏差。此外，通过回归系数获得了健康属性，这对患者护理具有重要潜在价值。
英文摘要：
Survival prediction for cancer patients is critical for optimal treatment selection and patient management. Current patient survival prediction methods typically extract survival information from patients' clinical record data or biological and imaging data. In practice, experienced clinicians can have a preliminary assessment of patients' health status based on patients' observable physical appearances, which are mainly facial features. However, such assessment is highly subjective. In this work, the efficacy of objectively capturing and using prognostic information contained in conventional portrait photographs using deep learning for survival predication purposes is investigated for the first time. A pre-trained StyleGAN2 model is fine-tuned on a custom dataset of our cancer patients' photos to empower its generator with generative ability suitable for patients' photos. The StyleGAN2 is then used to embed the photographs to its highly expressive latent space. Utilizing the state-of-the-art survival analysis models and based on StyleGAN's latent space photo embeddings, this approach achieved a C-index of 0.677, which is notably higher than chance and evidencing the prognostic value embedded in simple 2D facial images. In addition, thanks to StyleGAN's interpretable latent space, our survival prediction model can be validated for relying on essential facial features, eliminating any biases from extraneous information like clothing or background. Moreover, a health attribute is obtained from regression coefficients, which has important potential value for patient care.
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。
