一种替代性CellLineNet的初步探索：一种进化方法
A Preliminary Exploration into an Alternative CellLineNet: An Evolutionary Approach
摘要：
在本文中，探索一种替代CellLineNet的进化方法：一个卷积神经网络擅长上皮乳腺癌细胞系的分类。该进化算法引入了控制变量，该控制变量引导在反向残差块、瓶颈块、残差块和基本2 × 2卷积块的搜索空间中搜索架构。EvoCELL的承诺是预测什么样的特征提取块的组合或安排，为给定的任务产生最佳的模型架构。其中，示出了在每一代之后最适合的模型如何进化的性能。最终进化的模型CellLineNet V2分类了5种类型的上皮乳腺细胞系，包括两种人癌细胞系、2种正常永生化细胞系和1种永生化小鼠细胞系（MDA-MB-468、MCF 7、10A、12 A和HC 11）。多类细胞系分类卷积神经网络扩展了我们早期关于二进制乳腺癌细胞系分类模型的工作。本文提出了一个正在进行的探索性方法，神经网络的结构设计，并提出了进一步的研究。
英文摘要：
Within this paper, the exploration of an evolutionary approach to an alternative CellLineNet: a convolutional neural network adept at the classification of epithelial breast cancer cell lines, is presented. This evolutionary algorithm introduces control variables that guide the search of architectures in the search space of inverted residual blocks, bottleneck blocks, residual blocks and a basic 2x2 convolutional block. The promise of EvoCELL is predicting what combination or arrangement of the feature extracting blocks that produce the best model architecture for a given task. Therein, the performance of how the fittest model evolved after each generation is shown. The final evolved model CellLineNet V2 classifies 5 types of epithelial breast cell lines consisting of two human cancer lines, 2 normal immortalized lines, and 1 immortalized mouse line (MDA-MB-468, MCF7, 10A, 12A and HC11). The Multiclass Cell Line Classification Convolutional Neural Network extends our earlier work on a Binary Breast Cancer Cell Line Classification model. This paper presents an on-going exploratory approach to neural network architecture design and is presented for further study.
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