论文标题
对替代细胞网林的初步探索：进化方法
A Preliminary Exploration into an Alternative CellLineNet: An Evolutionary Approach
论文作者
论文摘要
在本文中，提出了对替代细胞网林的进化方法的探索：介绍了熟悉上皮乳腺癌细胞系的卷积神经网络。该进化算法引入了控制变量，该变量可以指导倒置残差块，瓶颈块，残留块和基本2x2卷积块的搜索空间中搜索体系结构的搜索。 Evocell的承诺是预测功能提取块的组合或布置，这些块为给定任务提供了最佳的模型体系结构。在其中，显示每一代后，优胜节模型如何演变的性能。最终进化的模型Celllinenet V2分类了5种类型的上皮乳腺细胞系，由两种人类癌系，2种正常永生线和1个永生的小鼠系组成（MDA-MB-468，MCF7、10A，12A，12A和HC11）。多类细胞系分类卷积神经网络扩展了我们在二元乳腺癌细胞系分类模型上的早期工作。本文介绍了一种正在进行的神经网络架构设计的探索方法，并进行了进一步研究。
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.
