  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.
