论文标题
LEFM NET：可学习的显式特征图深网，用于分割冷冻部分的组织病理学图像
LEFM-Nets: Learnable Explicit Feature Map Deep Networks for Segmentation of Histopathological Images of Frozen Sections
论文作者
论文摘要
准确的医学图像分割对于诊断和治疗疾病至关重要。这些问题是由高度复杂的模型（例如深网（DN））解决的，需要大量的标记数据进行培训。因此，许多DN具有特定于决策过程的任务或成像方式，通常很难解释和解释。在这里，我们提出了一个框架，将现有DNS嵌入由可学习的显式特征图（LEFM）层引起的低维子空间中。与现有的DN相比，该框架增加了一个高参数，并且仅适度增加了可学习参数的数量。该方法针对但不限于对低维医学图像的分割，例如染色冷冻切片的颜色组织病理学图像。由于LEFM层中的功能是原始特征的多项式函数，因此建议的LEFM-NET有助于网络决策的可解释性。在这项工作中，我们将LEFM与已知的网络相结合：DeepLabv3+，UNET，UNET ++和MA-NET。新的LEFM-NET应用于肝脏和曙红（H＆E）染色的冷冻切片的肝脏中结肠腺癌的分割。 LEFM-NET还对来自十个人体器官的H＆E染色冷冻切片的图像进行了测试。在第一个问题上，LEFM-NET在微平衡准确性和$ f_1 $得分方面取得了统计学上显着的性能提高，而不是原始网络。与第二个问题的原始网络相比，LEFM-NET只能达到更好的性能。源代码可在https://github.com/dsitnik/lefm上获得。
Accurate segmentation of medical images is essential for diagnosis and treatment of diseases. These problems are solved by highly complex models, such as deep networks (DN), requiring a large amount of labeled data for training. Thereby, many DNs possess task- or imaging modality specific architectures with a decision-making process that is often hard to explain and interpret. Here, we propose a framework that embeds existing DNs into a low-dimensional subspace induced by the learnable explicit feature map (LEFM) layer. Compared to the existing DN, the framework adds one hyperparameter and only modestly increase the number of learnable parameters. The method is aimed at, but not limited to, segmentation of low-dimensional medical images, such as color histopathological images of stained frozen sections. Since features in the LEFM layer are polynomial functions of the original features, proposed LEFM-Nets contribute to the interpretability of network decisions. In this work, we combined LEFM with the known networks: DeepLabv3+, UNet, UNet++ and MA-net. New LEFM-Nets are applied to the segmentation of adenocarcinoma of a colon in a liver from images of hematoxylin and eosin (H&E) stained frozen sections. LEFM-Nets are also tested on nuclei segmentation from images of H&E stained frozen sections of ten human organs. On the first problem, LEFM-Nets achieved statistically significant performance improvement in terms of micro balanced accuracy and $F_1$ score than original networks. LEFM-Nets achieved only better performance in comparison with the original networks on the second problem. The source code is available at https://github.com/dsitnik/lefm.
