ArticleHistochemistry and cell biology2022
Weighted average ensemble-based semantic segmentation in biological electron microscopy images.
Article in Histochemistry and cell biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Cell Structure Segmentation in TEM Images of Murine Skin Melanoma Cells by Deep Learning Model.Journal of imaging · 2026Article
- Leveraging Ensemble on Self-Supervised Techniques to Enhance Model Performance: A Regime Analysis.Journal of imaging informatics in medicine · 2026Article
- Classification of human epidermal growth factor receptor 2 expression in cancerous breast tissue through artificial intelligenceBiomedica : revista del Instituto Nacional de Salud · 2025Article
- DeepEM Playground: Bringing deep learning to electron microscopy labs.Journal of microscopy · 2025Article
- Deep ensemble learning for gastrointestinal diagnosis using endoscopic image classification.PeerJ. Computer science · 2025Article
- Modular segmentation, spatial analysis and visualization of volume electron microscopy datasets.Nature protocols · 2024Review
- A Wearable Assistant Device for the Hearing Impaired to Recognize Emergency Vehicle Sirens with Edge Computing.Sensors (Basel, Switzerland) · 2023Article
- GIT-Net: An Ensemble Deep Learning-Based GI Tract Classification of Endoscopic Images.Bioengineering (Basel, Switzerland) · 2023Article
- In focus in HCB.Histochemistry and cell biology · 2022Article
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Authors and funding
4 authors.
Funding
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 our model. Compared with a standard U-Net, the performance of our method is superior for all tested datasets. Furthermore, our model leverages a limited number of labeled training data to segment the electron microscopy images and therefore has a high potential for automated biological applications.
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