One of the main obstacles of adopting digital pathology is the challenge of efficient processing of hyperdimensional digitized biopsy samples, called whole slide images (WSIs). Exploiting deep learning and introducing compact WSI representations are urgently needed to accelerate image analysis and facilitate the visualization and interpretability of pathology results in a postpandemic world. In this paper, we introduce a new evolutionary approach for WSI representation based on large-scale multi-objective optimization (LSMOP) of deep embeddings. We start with patch-based sampling to feed KimiaNet , a histopathology-specialized deep network, and to extract a multitude of feature vectors. Coarse multi-objective feature selection uses the reduced search space strategy guided by the classification accuracy and the number of features. In the second stage, the frequent features histogram (FFH), a novel WSI representation, is constructed by multiple runs of coarse LSMOP. Fine evolutionary feature selection is then applied to find a compact (short-length) feature vector based on the FFH and contributes to a more robust deep-learning approach to digital pathology supported by the stochastic power of evolutionary algorithms. We validate the proposed schemes using The Cancer Genome Atlas (TCGA) images in terms of WSI representation, classification accuracy, and feature quality. Furthermore, a novel decision space for multicriteria decision making in the LSMOP field is introduced. Finally, a patch-level visualization approach is proposed to increase the interpretability of deep features. The proposed evolutionary algorithm finds a very compact feature vector to represent a WSI (almost 14,000 times smaller than the original feature vectors) with 8% higher accuracy compared to the codes provided by the state-of-the-art methods.

翻译：摘要：采纳数字病理学的主要障碍之一在于高效处理超大规模数字化活检样本（即全切片图像，WSIs）的挑战。利用深度学习并引入紧凑型WSI表征，在疫情后世界中对于加速图像分析、促进病理结果的可视化与可解释性具有迫切需求。本文提出一种基于大规模多目标优化（LSMOP）的深度嵌入进化方法用于WSI表征。我们首先进行基于补丁的采样，以馈入专用于组织病理学的深度网络KimiaNet，并提取大量特征向量。粗粒度多目标特征选择采用基于分类精度与特征数量的缩减搜索空间策略。在第二阶段，通过多次运行粗粒度LSMOP构建了一种新型WSI表征——频繁特征直方图（FFH）。随后应用细粒度进化特征选择，基于FFH寻找紧凑（短长度）特征向量，并借助进化算法的随机能力，为数字病理学中更稳健的深度学习方法提供支持。我们使用癌症基因组图谱（TCGA）图像，从WSI表征、分类精度和特征质量方面验证了所提方案。此外，在LSMOP领域引入了一种新的多准则决策空间。最后，提出了一种补丁级可视化方法以增强深度特征的可解释性。所提出的进化算法能够找到极其紧凑的特征向量来表征WSI（较原始特征向量缩小近14,000倍），同时与现有最优方法提供的编码相比，精度提升8%。