Learning Low-dimensional Manifolds for Scoring of Tissue Microarray Images

TL;DR

This paper introduces mfTacoma, a novel semi-supervised deep learning method that learns low-dimensional manifolds to improve the scoring accuracy of tissue microarray images, especially with limited data.

Contribution

mfTacoma is the first approach to incorporate manifold learning into deep representations for TMA image scoring, enhancing performance with small datasets.

Findings

mfTacoma outperforms PCA-based deep features in TMA scoring.

Manifold-based deep features improve accuracy over group property-based features.

The method effectively leverages manifold information in small data regimes.

Abstract

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.