MAg: a simple learning-based patient-level aggregation method for detecting microsatellite instability from whole-slide images
This work addresses a cost-efficient MSI detection method for gastrointestinal cancer patients, but it is incremental as it focuses on improving the aggregation stage of existing deep learning pipelines.
The paper tackles the problem of detecting microsatellite instability (MSI) from whole-slide images by proposing a simple patient-level aggregation method called MAg, which improves accuracy over naive statistical methods on two public datasets.
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 naïve 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.