Tissue microarray (TMA) images have emerged as an important high-throughput tool for cancer study and the validation of biomarkers. Efforts have been dedicated to further improve the accuracy of TACOMA, a cutting-edge automatic scoring algorithm for TMA images. One major advance is due to deepTacoma, an algorithm that incorporates suitable deep representations of a group nature. Inspired by the recent advance in semi-supervised learning and deep learning, we propose mfTacoma to learn alternative deep representations in the context of TMA image scoring. In particular, mfTacoma learns the low-dimensional manifolds, a common latent structure in high dimensional data. Deep representation learning and manifold learning typically requires large data. By encoding deep representation of the manifolds as regularizing features, mfTacoma effectively leverages the manifold information that is potentially crude due to small data. Our experiments show that deep features by manifolds outperforms two alternatives -- deep features by linear manifolds with principal component analysis or by leveraging the group property.

中文翻译：

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学习用于组织微阵列图像评分的低维流形。

组织微阵列（TMA）图像已成为癌症研究和生物标志物验证的重要高通量工具。致力于进一步提高TACOMA的准确性，TACOMA是针对TMA图像的一种先进的自动评分算法。一个主要的进步是由于deepTacoma，该算法结合了适当的群体性质的深层表示。受半监督学习和深度学习的最新进展启发，我们建议mfTacoma在TMA图像评分的背景下学习替代性深度表示。特别是，mfTacoma学习低维流形，这是高维数据中常见的潜在结构。深度表示学习和多种学习通常需要大数据。通过将流形的深层表示编码为正则化特征，mfTacoma有效地利用了由于数据量少而潜在的原始信息。我们的实验表明，流形的深层特征优于两个替代方案-带有主成分分析的线性流形或通过利用基团属性的深层特征。