Cinepro：面向前列腺超声电影回放中癌症检测的基础模型鲁棒训练
Cinepro: Robust Training of Foundation Models for Cancer Detection in Prostate Ultrasound Cineloops
AI总结：
本文提出 Cinepro 框架，通过融合活检核心癌组织比例的鲁棒损失与跨帧时间增强，训练基础模型在弱标注前列腺超声电影回放中定位癌症，并在多中心数据集上超越现有基准。
摘要：
使用深度学习（DL）模型进行前列腺癌（PCa）检测，已显示出增强活检期间实时引导的潜力。然而，前列腺超声图像缺乏像素级癌症标注，从而引入标签噪声。当前方法通常聚焦于有限的感兴趣区域（ROIs），忽视了准确诊断所需的解剖学上下文。基础模型可通过分析整幅图像以捕获全局空间关系来克服这一局限；但它们仍面临超声数据中与粗略病理标注相关的弱标签所带来的挑战。我们提出 Cinepro，这是一种增强基础模型在超声电影回放中定位 PCa 能力的新颖框架。Cinepro 通过将病理报告的活检核心中癌组织比例纳入其损失函数来适配鲁棒训练，以应对标签噪声，提供更细致的监督。此外，它利用跨多帧的时间数据实施鲁棒增强，提升模型学习稳定癌症相关特征的能力。Cinepro 在一个多中心前列腺超声数据集上表现出优越性能，取得 77.1% 的 AUROC 和 83.8% 的平衡准确率，超过当前基准。这些发现强调了 Cinepro 在推进面向弱标签超声数据的基础模型方面的前景。
英文摘要：
Prostate cancer (PCa) detection using deep learning (DL) models has shown potential for enhancing real-time guidance during biopsies. However, prostate ultrasound images lack pixel-level cancer annotations, introducing label noise. Current approaches often focus on limited regions of interest (ROIs), disregarding anatomical context necessary for accurate diagnosis. Foundation models can overcome this limitation by analyzing entire images to capture global spatial relationships; however, they still encounter challenges stemming from the weak labels associated with coarse pathology annotations in ultrasound data. We introduce Cinepro, a novel framework that strengthens foundation models' ability to localize PCa in ultrasound cineloops. Cinepro adapts robust training by integrating the proportion of cancer tissue reported by pathology in a biopsy core into its loss function to address label noise, providing a more nuanced supervision. Additionally, it leverages temporal data across multiple frames to apply robust augmentations, enhancing the model's ability to learn stable cancer-related features. Cinepro demonstrates superior performance on a multi-center prostate ultrasound dataset, achieving an AUROC of 77.1% and a balanced accuracy of 83.8%, surpassing current benchmarks. These findings underscore Cinepro's promise in advancing foundation models for weakly labeled ultrasound data.
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