Most efforts in Computer Vision focus on natural images or artwork, which differ significantly both in size and contents from the kind of data biomedical image processing deals with. Thus, Transfer Learning models often prove themselves suboptimal for these tasks, even after manual finetuning. The development of architectures from scratch is oftentimes unfeasible due to the vastness of the hyperparameter space and a shortage of time, computational resources and Deep Learning experts in most biomedical research laboratories. An alternative to manually defining the models is the use of Neuroevolution, which employs metaheuristic techniques to optimize Deep Learning architectures. However, many algorithms proposed in the neuroevolutive literature are either too unreliable or limited to a small, predefined region of the hyperparameter space. To overcome these shortcomings, we propose the Chimera Algorithm, a novel, hybrid neuroevolutive algorithm that integrates the Artificial Bee Colony Algorithm with Evolutionary Computation tools to generate models from scratch, as well as to refine a given previous architecture to better fit the task at hand. The Chimera Algorithm has been validated with two datasets of natural and medical images, producing models that surpassed the performance of those coming from Transfer Learning.

翻译：计算机视觉领域的大部分研究聚焦于自然图像或艺术作品，这类数据在尺寸和内容上与生物医学图像处理所涉及的数据存在显著差异。因此，即使经过手动微调，迁移学习模型往往仍无法在这些任务中取得最优效果。由于超参数空间庞大，加之大多数生物医学实验室缺乏时间、计算资源和深度学习专家，从头设计架构往往不可行。手动定义模型的另一种替代方案是使用神经进化——通过元启发式技术优化深度学习架构。然而，现有神经进化文献中提出的许多算法要么过于不可靠，要么局限于超参数空间中一个较小的预定区域。为克服这些缺陷，我们提出了Chimera算法——一种新颖的混合神经进化算法，该算法将人工蜂群算法与进化计算工具相结合，可从头生成模型，也可对现有架构进行优化以使其更适配当前任务。通过两个自然图像与医学图像数据集验证，Chimera算法生成的模型在性能上超越了迁移学习模型。