BEDS: Bagging ensemble deep segmentation for nucleus segmentation with testing stage stain augmentation
This work addresses nucleus segmentation in medical imaging, which is incremental as it adapts existing ensemble techniques to deep learning for a specific domain.
The paper tackled the problem of reducing outcome variance in deep learning-based medical image analysis by proposing a bagging ensemble deep segmentation (BEDS) method for nucleus segmentation on pathological images, achieving superior segmentation performance through complementary strategies of self-ensemble learning and testing stage stain augmentation.
Reducing outcome variance is an essential task in deep learning based medical image analysis. Bootstrap aggregating, also known as bagging, is a canonical ensemble algorithm for aggregating weak learners to become a strong learner. Random forest is one of the most powerful machine learning algorithms before deep learning era, whose superior performance is driven by fitting bagged decision trees (weak learners). Inspired by the random forest technique, we propose a simple bagging ensemble deep segmentation (BEDs) method to train multiple U-Nets with partial training data to segment dense nuclei on pathological images. The contributions of this study are three-fold: (1) developing a self-ensemble learning framework for nucleus segmentation; (2) aggregating testing stage augmentation with self-ensemble learning; and (3) elucidating the idea that self-ensemble and testing stage stain augmentation are complementary strategies for a superior segmentation performance. Implementation Detail: https://github.com/xingli1102/BEDs.