Elytra 幻影——利用深度学习从隐翅虫图像中进行系统发育性状提取——仅有掩码就足够了吗？
The Phantom of the Elytra -- Phylogenetic Trait Extraction from Images of Rove Beetles Using Deep Learning -- Is the Mask Enough?
AI总结：
本文比较了完整分割、二值掩码和傅里叶描述子三种形态表征在隐翅虫系统发育性状提取中的效果，发现基于掩码的 ResNet50 模型表现最佳，强调了选择合适形态表征的重要性。
摘要：
系统发育分析传统上依赖劳动密集的人工形态性状提取，这限制了其在大规模数据集上的可扩展性。近年来深度学习的进展为自动化这一过程提供了可能，但不同形态表征在系统发育性状提取中的有效性仍不清楚。在本研究中，我们比较了使用三种不同形态表征的深度学习模型的性能——完整分割、二值掩码和甲虫轮廓的傅里叶描述子。我们在 Rove-Tree-11 数据集上进行了测试，该数据集是来自 215 个隐翅虫物种的图像精选集合。我们的结果表明，基于掩码的模型优于其他模型，在测试集上实现了 0.33 ± 0.02 的归一化 Align Score，而基于傅里叶的模型为 0.45 ± 0.01，基于分割的模型为 0.39 ± 0.07。基于掩码的模型的性能可能反映了其在利用 ResNet50 架构的深度和容量的同时捕捉形状特征的能力。这些结果还表明，至少在这类甲虫中，背面纹理特征可能具有较低的系统发育相关性，尽管还需要进一步研究来证实这一点。相比之下，基于傅里叶的模型受到容量降低和轮廓近似中偶发不准确的影响，特别是在腿等精细结构上。这些发现强调了为自动化系统发育研究选择合适形态表征的重要性，以及在自动形态性状提取中进一步研究可解释性的必要性。
英文摘要：
Phylogenetic analysis traditionally relies on labor-intensive manual extraction of morphological traits, limiting its scalability for large datasets. Recent advances in deep learning offer the potential to automate this process, but the effectiveness of different morphological representations for phylogenetic trait extraction remains poorly understood. In this study, we compare the performance of deep learning models using three distinct morphological representations - full segmentations, binary masks, and Fourier descriptors of beetle outlines. We test this on the Rove-Tree-11 dataset, a curated collection of images from 215 rove beetle species. Our results demonstrate that the mask-based model outperformed the others, achieving a normalized Align Score of 0.33 plus/minus 0.02 on the test set, compared to 0.45 plus/minus 0.01 for the Fourier-based model and 0.39 plus/minus 0.07 for the segmentation-based model. The performance of the mask-based model likely reflects its ability to capture shape features while taking advantage of the depth and capacity of the ResNet50 architecture. These results also indicate that dorsal textural features, at least in this group of beetles, may be of lowered phylogenetic relevance, though further investigation is necessary to confirm this. In contrast, the Fourier-based model suffered from reduced capacity and occasional inaccuracies in outline approximations, particularly in fine structures like legs. These findings highlight the importance of selecting appropriate morphological representations for automated phylogenetic studies and the need for further research into explainability in automatic morphological trait extraction.
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