CountXplain: 基于原型的密度图估计用于可解释的细胞计数
CountXplain: Interpretable Cell Counting with Prototype-Based Density Map Estimation
AI总结：
CountXplain通过原型密度图估计实现可解释的细胞计数，结合生物学家验证的原型模式，提升模型透明度和计数效果。
摘要：
生物医学影像中的细胞计数对于各种临床应用至关重要，然而在该领域深度学习模型的可解释性仍是一个重大挑战。我们提出了一种新颖的基于原型的方法，通过密度图估计实现可解释的细胞计数。我们的方法将原型层整合到密度估计网络中，使模型能够学习用于细胞和背景伪影的代表性视觉模式。通过生物学家的调查，验证了所学习的原型模式的相关性，进一步验证了模型的可解释性。通过生成突出显示输入图像中与每个原型最相似区域的解释，我们的方法提供了对模型如何识别和计数细胞的清晰理解。在两个公开数据集上的广泛实验表明，我们的方法在不牺牲计数有效性的情况下实现了可解释性。这项工作为研究人员和临床医生提供了一个透明且可靠的细胞计数工具，可能提高对深度学习在关键生物医学应用中信任度并加速其采用。代码可在https://github.com/NRT-D4/CountXplain获取。
英文摘要：
Cell counting in biomedical imaging is pivotal for various clinical applications, yet the interpretability of deep learning models in this domain remains a significant challenge. We propose a novel prototype-based method for interpretable cell counting via density map estimation. Our approach integrates a prototype layer into the density estimation network, enabling the model to learn representative visual patterns for both cells and background artifacts. The learned prototypes were evaluated through a survey of biologists, who confirmed the relevance of the visual patterns identified, further validating the interpretability of the model. By generating interpretations that highlight regions in the input image most similar to each prototype, our method offers a clear understanding of how the model identifies and counts cells. Extensive experiments on two public datasets demonstrate that our method achieves interpretability without compromising counting effectiveness. This work provides researchers and clinicians with a transparent and reliable tool for cell counting, potentially increasing trust and accelerating the adoption of deep learning in critical biomedical applications. Code is available at https://github.com/NRT-D4/CountXplain.
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。
