Abstract
Self-supervised learning (SSL) has emerged as a promising technique for medical image analysis due to its ability to learn without annotations. However, despite the promising potential, conventional SSL methods encounter limitations, including challenges in achieving semantic alignment and capturing subtle details. This leads to suboptimal representations, which fail to accurately capture the underlying anatomical structures and pathological details. In response to these constraints, we introduce a novel SSL framework OPTiML, employing optimal transport (OT), to capture the dense semantic invariance and fine-grained details, thereby enhancing the overall effectiveness of SSL in medical image representation learning. The core idea is to integrate OT with a cross-viewpoint semantics infusion module (CV-SIM), which effectively captures complex, fine-grained details inherent in medical images across different viewpoints. In addition to the CV-SIM module, OPTiML imposes the variance and covariance regularizations within OT framework to force the model focus on clinically relevant information while discarding less informative features. Through these, the proposed framework demonstrates its capacity to learn semantically rich representations that can be applied to various medical imaging tasks. To validate its effectiveness, we conduct experimental studies on three publicly available datasets from chest X-ray modality. Our empirical results reveal OPTiML's superiority over state-of-the-art methods across all evaluated tasks.
Abstract (translated)
自监督学习（SSL）作为一种无需标注的学习技术，在医学图像分析领域呈现出巨大的潜力。然而，尽管具有潜在的积极影响，传统的 SSL 方法也存在局限性，包括在实现语义对齐和捕捉细微细节方面遇到的挑战。这导致 suboptimal 表示，无法准确捕捉到解剖学结构和病理细节。为了应对这些限制，我们引入了一个名为 OPTiML 的新 SSL 框架，采用最优传输（OT）技术，以捕捉密集的语义不变性和细粒度细节，从而增强 SSL 在医学图像表示学习中的整体效果。核心思想是将 OT 与跨视点语义注入模块（CV-SIM）相结合，有效地捕捉不同观点下医学图像中复杂、细粒度的细节。除了 CV-SIM 模块之外，OPTiML 对 OT 框架内的方差和协方差进行正则化，以迫使模型将注意力集中在临床相关信息上，而忽略更不相关的特征。通过这些，所提出的框架展示了其学习语义丰富表示的能力，可以应用于各种医学成像任务。为了验证其有效性，我们在三个公开可用的数据集（包括胸部 X 光摄影模式）上进行了实验研究。我们的实证结果表明，OPTiML 在所有评估任务上都优于最先进的 methods。
URL
https://arxiv.org/abs/2404.11868
