ProFound是专用于前列腺影像的中等规模视觉基础模型，可通用处理多种临床任务。
ProFound: A moderate-sized vision foundation model for multi-task prostate imaging
- 基于自监督学习，在5000名患者、2.2万份3D MRI上预训练
- 11项临床任务上性能超越或媲美专用模型，泛化能力强
- 适合医疗影像研发者快速部署多任务解决方案
前列腺癌的诊断与治疗日益依赖多参数MRI。自动化虽有进展，但受限于需大量标注数据且任务间难以迁移。本文提出ProFound，一种针对体积化前列腺mpMRI的领域专用视觉基础模型。该模型在包含5,000名患者的多机构数据集上，通过多种自监督方法预训练，涵盖超过22,000个独特的3D MRI体积（超180万张2D图像切片）。我们在超过3,000名独立患者上系统评估了其在11项下游临床任务中的表现，包括前列腺癌检测、格里森分级、病灶定位、腺体体积估计及分区与周围结构分割。实验表明，微调后的ProFound在各项任务中均优于或媲美现有最先进专用模型和同类医学视觉基础模型，展现出优异的泛化能力。
原文摘要 · Abstract (English)
Many diagnostic and therapeutic clinical tasks for prostate cancer increasingly rely on multi-parametric MRI. Automating these tasks is challenging because they necessitate expert interpretations, which are difficult to scale to capitalise on modern deep learning. Although modern automated systems achieve expert-level performance in isolated tasks, their general clinical utility remains limited by the requirement of large task-specific labelled datasets. In this paper, we present ProFound, a domain-specialised vision foundation model for volumetric prostate mpMRI. ProFound is pre-trained using several variants of self-supervised approaches on a diverse, multi-institutional collection of 5,000 patients, with a total of over 22,000 unique 3D MRI volumes (over 1,800,000 2D image slices). We conducted a systematic evaluation of ProFound across a broad spectrum of $11$ downstream clinical tasks on over 3,000 independent patients, including prostate cancer detection, Gleason grading, lesion localisation, gland volume estimation, zonal and surrounding structure segmentation. Experimental results demonstrate that finetuned ProFound consistently outperforms or remains competitive with state-of-the-art specialised models and existing medical vision foundation models trained/finetuned on the same data.
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