# Incorporating Deep Features in the Analysis of Tissue Microarray Images

## TL;DR

This paper enhances the TACOMA algorithm for tissue microarray image analysis by integrating deep, learnable features through unsupervised clustering and spatial partitioning, reducing error rates and addressing heterogeneity and noise.

## Contribution

It introduces a novel approach of incorporating deep, learnable representations into TMA image scoring, improving accuracy and robustness over existing methods.

## Key findings

- Reduced TACOMA error rate by about 6% on breast cancer TMA images
- Demonstrated effectiveness of unsupervised clustering in handling heterogeneity
- Provided insights on when deep representations improve model performance

## Abstract

Tissue microarray (TMA) images have been used increasingly often in cancer studies and the validation of biomarkers. TACOMA---a cutting-edge automatic scoring algorithm for TMA images---is comparable to pathologists in terms of accuracy and repeatability. Here we consider how this algorithm may be further improved. Inspired by the recent success of deep learning, we propose to incorporate representations learnable through computation. We explore representations of a group nature through unsupervised learning, e.g., hierarchical clustering and recursive space partition. Information carried by clustering or spatial partitioning may be more concrete than the labels when the data are heterogeneous, or could help when the labels are noisy. The use of such information could be viewed as regularization in model fitting. It is motivated by major challenges in TMA image scoring---heterogeneity and label noise, and the cluster assumption in semi-supervised learning. Using this information on TMA images of breast cancer, we have reduced the error rate of TACOMA by about 6%. Further simulations on synthetic data provide insights on when such representations would likely help. Although we focus on TMAs, learnable representations of this type are expected to be applicable in other settings.