PCIM：通过像素级通道隔离混合学习像素属性
PCIM: Learning Pixel Attributions via Pixel-wise Channel Isolation Mixing in High Content Imaging
AI总结：
PCIM通过像素级通道隔离混合方法，无需内部网络状态或梯度，生成像素属性图，提升高内容成像中的模型保真度和定位能力。
摘要：
深度神经网络（DNNs）在各种计算机视觉任务中表现出显著的成功。然而，它们的黑箱性质往往导致解释决策的困难，从而产生了解释决策方法的迫切需求，最终形成在生物医学应用中广泛应用的障碍。本文介绍了一种新方法，像素级通道隔离混合（PCIM），用于计算像素属性图，突出对分类决策最关键的图像部分，但无需提取内部网络状态或梯度。与现有方法不同，PCIM将每个像素视为一个独立的输入通道，并训练一个混合层来混合这些像素，反映特定的分类。这种方法的独特之处在于能够为每张图像生成像素属性图，但不依赖于底层分类网络的选择。在三个应用相关、多样化的高内容成像数据集上的基准测试显示了最先进的性能，特别是在荧光和明场高内容成像中的模型保真度和定位能力方面。PCIM作为创建任意DNNs像素级属性图的独特且有效的方法，使可解释性和信任成为可能。
英文摘要：
Deep Neural Networks (DNNs) have shown remarkable success in various computer vision tasks. However, their black-box nature often leads to difficulty in interpreting their decisions, creating an unfilled need for methods to explain the decisions, and ultimately forming a barrier to their wide acceptance especially in biomedical applications. This work introduces a novel method, Pixel-wise Channel Isolation Mixing (PCIM), to calculate pixel attribution maps, highlighting the image parts most crucial for a classification decision but without the need to extract internal network states or gradients. Unlike existing methods, PCIM treats each pixel as a distinct input channel and trains a blending layer to mix these pixels, reflecting specific classifications. This unique approach allows the generation of pixel attribution maps for each image, but agnostic to the choice of the underlying classification network. Benchmark testing on three application relevant, diverse high content Imaging datasets show state-of-the-art performance, particularly for model fidelity and localization ability in both, fluorescence and bright field High Content Imaging. PCIM contributes as a unique and effective method for creating pixel-level attribution maps from arbitrary DNNs, enabling interpretability and trust.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
