Modality-Classification of Microscopy Images Using Shallow Variants of Deep Networks
Modality-Classification of Microscopy Images Using Shallow Variants of Deep Networks
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DOI：
10.1109/bibm49941.2020.9313467
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发表时间：
2020-12
期刊：
影响因子：
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通讯作者：
J. Trabucco;Pengyuan Li;C. Arighi;H. Shatkay;G. Marai
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文献类型：
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作者：
J. Trabucco;Pengyuan Li;C. Arighi;H. Shatkay;G. Marai
显微镜图像在生物医学研究出版物中很普遍，其中通过各种显微镜模式（光，荧光，扫描，透射）获得的图像通常用于描述和总结实验和贡献。因此，自动识别这些显微镜图像的模态并在自动搜索工具中利用这些知识的兴趣越来越大。然而，识别显微图像带来了挑战，由于缺乏广泛的集合标记的图像。我们描述和评估两种替代方法显微图像分类。在第一种方法中，我们逐步微调ResNet模型的层。第二种方法使用ResNet网络的浅层变体，我们利用以前卷积块的输出。我们将这些结果与基于支持向量机（SVM）的基线进行比较。我们的研究结果表明，微调特定层产生更好的结果比微调整个模型。此外，与整个微调模型相比，较浅的变体产生有竞争力的结果。
Microscopy images are pervasive in biomedical research publications, where images obtained through various microscopy modalities (light, fluorescence, scanning, transmission) are often used to describe and summarize experiments and contributions. Hence, there is growing interest in automatically identifying these microscopy images’ modality and utilizing this knowledge in automated search tools. However, identifying microscopy images poses challenges due to a lack of extensive collections of labeled images. We describe and evaluate two alternative approaches to microscopy image classification. In the first approach, we progressively fine-tuned layers of ResNet models. The second approach uses shallow variants of ResNet networks, where we leverage the outputs from previous convolutional blocks. We compare these results against a Support Vector Machine (SVM)-based baseline. Our results show that fine-tuning specific layers yields better results than fine-tuning the whole model. Furthermore, shallower variants produce competitive results when compared to the entire fine-tuned model.