标题： Cinepro：前列腺超声癌症检测基础模型的稳健训练 Cineloops 显示英文标题

标题： Cinepro: Robust Training of Foundation Models for Cancer Detection in Prostate Ultrasound Cineloops

摘要： 前列腺癌（PCa）检测使用深度学习（DL）模型在增强活检过程中的实时指导方面显示出潜力。然而，前列腺超声图像缺乏像素级别的癌症标注，导致标签噪声的引入。目前的方法通常集中在有限的兴趣区域（ROIs），忽略了准确诊断所需的解剖学背景。基础模型可以通过分析整个图像来捕捉全局空间关系以克服这一限制；然而，它们仍然面临来自超声数据中粗略病理标注所关联的弱标签带来的挑战。我们介绍了Cinepro，这是一种新颖的框架，增强了基础模型在超声动态图像中定位PCa的能力。Cinepro通过将病理报告中标记的活检核心中癌症组织的比例整合到其损失函数中，以适应鲁棒训练，从而解决标签噪声问题，提供更细致的监督。此外，它利用多帧中的时间数据应用鲁棒增强，提高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色，达到了77.1%的AUROC和83.8%的平衡准确率，超越了当前基准。这些发现强调了Cinepro在推进弱标签超声数据的基础模型方面的前景。 显示英文摘要

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.