基于异常驱动的放射影像表示学习
Abnormality-Driven Representation Learning for Radiology Imaging
AI总结：
针对放射学缺乏任务无关表示模型的问题，提出CLEAR框架结合病变增强对比学习（LeCL），在三个临床任务上优于现有基础模型且计算和数据效率更高。
摘要：
迄今为止，放射学深度学习流水线最常用的方法是使用基于其他任务预训练模型的端到端3D网络，然后针对当前任务进行微调。相比之下，病理学等专注于2D图像的相邻医学领域已有效采用基于自监督学习（SSL）的任务无关基础模型，并结合弱监督深度学习（DL）。然而，由于3D成像的计算和数据需求以及放射扫描固有的解剖复杂性，放射学领域仍缺乏任务无关的表示模型。为解决这一差距，我们提出CLEAR，一个利用2D切片提取嵌入并结合基于注意力的聚合来高效预测临床终点的放射影像框架。作为该框架的一部分，我们引入病变增强对比学习（LeCL），这是一种通过CT扫描不同位置的2D轴向切片中的异常来获取视觉表示的新方法。具体而言，我们使用三种不同架构训练单域对比学习方法：Vision Transformers、Vision State Space Models和Gated Convolutional Neural Networks。我们在三个临床任务上评估该方法：肿瘤病变定位、肺部疾病检测和患者分期，并与包括BiomedCLIP在内的四种最先进基础模型进行基准测试。研究结果表明，使用LeCL学习的表示的CLEAR优于现有基础模型，同时在计算和数据效率上显著更高。
英文摘要：
To date, the most common approach for radiology deep learning pipelines is the use of end-to-end 3D networks based on models pre-trained on other tasks, followed by fine-tuning on the task at hand. In contrast, adjacent medical fields such as pathology, which focus on 2D images, have effectively adopted task-agnostic foundational models based on self-supervised learning (SSL), combined with weakly-supervised deep learning (DL). However, the field of radiology still lacks task-agnostic representation models due to the computational and data demands of 3D imaging and the anatomical complexity inherent to radiology scans. To address this gap, we propose CLEAR, a framework for radiology images that uses extracted embeddings from 2D slices along with attention-based aggregation for efficiently predicting clinical endpoints. As part of this framework, we introduce lesion-enhanced contrastive learning (LeCL), a novel approach to obtain visual representations driven by abnormalities in 2D axial slices across different locations of the CT scans. Specifically, we trained single-domain contrastive learning approaches using three different architectures: Vision Transformers, Vision State Space Models and Gated Convolutional Neural Networks. We evaluate our approach across three clinical tasks: tumor lesion location, lung disease detection, and patient staging, benchmarking against four state-of-the-art foundation models, including BiomedCLIP. Our findings demonstrate that CLEAR using representations learned through LeCL, outperforms existing foundation models, while being substantially more compute- and data-efficient.
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