Weighted average ensemble-based semantic segmentation in biological electron microscopy images.

ABSTRACT: Semantic segmentation of electron microscopy images using deep learning methods is a valuable tool for the detailed analysis of organelles and cell structures. However, these methods require a large amount of labeled ground truth data that is often unavailable. To address this limitation, we present a weighted average ensemble model that can automatically segment biological structures in electron microscopy images when trained with only a small dataset. Thus, we exploit the fact that a combination of diverse base-learners is able to outperform one single segmentation model. Our experiments with seven different biological electron microscopy datasets demonstrate quantitative and qualitative improvements. We show that the Grad-CAM method can be used to interpret and verify the prediction of

SUBMITTER: Shaga Devan K 

PROVIDER: S-EPMC9630254 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature