Currently, analysis of microscopic In Situ Hybridization images is done manually by experts. Precise evaluation and classification of such microscopic images can ease experts' work and reveal further insights about the data. In this work, we propose a deep-learning framework to detect and classify areas of microscopic images with similar levels of gene expression. The data we analyze requires an unsupervised learning model for which we employ a type of Artificial Neural Network - Deep Learning Autoencoders. The model's performance is optimized by balancing the latent layers' length and complexity and fine-tuning hyperparameters. The results are validated by adapting the mean-squared error (MSE) metric, and comparison to expert's evaluation.

翻译：目前，显微原位杂交图像的分析主要由专家手动完成。对此类显微图像的精确评估与分类不仅能减轻专家的工作负担，还能揭示数据中更深入的见解。本文提出了一种深度学习框架，用于检测并分类具有相似基因表达水平的显微图像区域。由于所分析数据需要无监督学习模型，我们采用了一种人工神经网络——深度学习自动编码器。通过平衡潜在层的长度与复杂度并微调超参数，优化了模型性能。结果通过调整均方误差（MSE）指标并与专家评估进行对比验证。