  The prediction of microsatellite instability (MSI) and microsatellite
stability (MSS) is essential in predicting both the treatment response and
prognosis of gastrointestinal cancer. In clinical practice, a universal MSI
testing is recommended, but the accessibility of such a test is limited. Thus,
a more cost-efficient and broadly accessible tool is desired to cover the
traditionally untested patients. In the past few years, deep-learning-based
algorithms have been proposed to predict MSI directly from haematoxylin and
eosin (H&E)-stained whole-slide images (WSIs). Such algorithms can be
summarized as (1) patch-level MSI/MSS prediction, and (2) patient-level
aggregation. Compared with the advanced deep learning approaches that have been
employed for the first stage, only the na\"ive first-order statistics (e.g.,
averaging and counting) were employed in the second stage. In this paper, we
propose a simple yet broadly generalizable patient-level MSI aggregation (MAg)
method to effectively integrate the precious patch-level information. Briefly,
the entire probabilistic distribution in the first stage is modeled as
histogram-based features to be fused as the final outcome with machine learning
(e.g., SVM). The proposed MAg method can be easily used in a plug-and-play
manner, which has been evaluated upon five broadly used deep neural networks:
ResNet, MobileNetV2, EfficientNet, Dpn and ResNext. From the results, the
proposed MAg method consistently improves the accuracy of patient-level
aggregation for two publicly available datasets. It is our hope that the
proposed method could potentially leverage the low-cost H&E based MSI detection
method. The code of our work has been made publicly available at
https://github.com/Calvin-Pang/MAg.
