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:数字病理学采用的主要挑战之一是高维数字化活检样本的有效处理,称为全幻灯片图像(WSI)。利用深度学习并引入紧凑的WSI表示形式亟需加速图像分析,促进在后疫情时代病理结果的可视化和可解释性。在本文中,我们介绍了一种新的WSI表示方法,基于深度嵌入的大规模多目标优化(LSMOP)的进化方法。我们从基于补丁的采样开始,以提供KimiaNet,一种专门针对组织病理学的深度网络,并提取多个特征向量。粗糙的多目标特征选择采用受分类精度和特征数量引导的降低搜索空间策略。在第二阶段中,通过多次粗糙LSMOP构建了一种新型的WSI表示形式——频繁特征直方图(FFH)。然后,对FFH进行精细的进化特征选择,以基于FFH找到一个更紧凑(长度短)的特征向量,并为数字病理学的更强大的深度学习方法做出贡献,支持进化算法的随机功率。我们使用癌症基因组图谱(TCGA)映像验证了所提出的方案在WSI表示、分类精度和特征质量方面。此外,还介绍了LSMOP领域多标准决策的新决策空间,并提出了一种基于补丁级的可视化方法,以增加深度特征的可解释性。所提出的进化算法找到了一个非常紧凑的特征向量来表示WSI(比原始特征向量小了近14,000倍),比最先进方法提供的代码的精度高8%。