标题： 基于深度学习的CT分析的可解释自动窗口设置 显示英文标题

标题： Interpretable Auto Window Setting for Deep-Learning-Based CT Analysis

摘要： 无论是在CT普及的早期还是现在，CT中的窗宽设置始终是CT分析过程中的不可或缺部分。 尽管研究已经探讨了CT多窗融合在增强神经网络方面的能力，但目前仍缺乏领域不变且直观可解释的自动窗宽设置方法。 在本工作中，我们提出一个源自双曲正切激活函数的即插即用模块，该模块与主流深度学习架构兼容。 从CT的物理原理出发，我们坚持可解释性原则，以确保模块在医疗应用中的可靠性。 领域不变的设计有助于从临床上直观的角度观察自适应机制所做的偏好决策。 这使得所提出的方法不仅能够被神经网络专家理解，还能获得临床医生更高的信任。 我们在多个开源数据集中验证了所提出方法的有效性，在困难分割目标上取得了10%~200%的Dice分数提升。 显示英文摘要

摘要： Whether during the early days of popularization or in the present, the window setting in Computed Tomography (CT) has always been an indispensable part of the CT analysis process. Although research has investigated the capabilities of CT multi-window fusion in enhancing neural networks, there remains a paucity of domain-invariant, intuitively interpretable methodologies for Auto Window Setting. In this work, we propose an plug-and-play module originate from Tanh activation function, which is compatible with mainstream deep learning architectures. Starting from the physical principles of CT, we adhere to the principle of interpretability to ensure the module's reliability for medical implementations. The domain-invariant design facilitates observation of the preference decisions rendered by the adaptive mechanism from a clinically intuitive perspective. This enables the proposed method to be understood not only by experts in neural networks but also garners higher trust from clinicians. We confirm the effectiveness of the proposed method in multiple open-source datasets, yielding 10%~200% Dice improvements on hard segment targets.