ArticleProceedings. IEEE International Conference on Bioinformatics and Biomedicine2022
The Combined Focal Cross Entropy and Dice Loss Function for Segmentation of Protein Secondary Structures from Cryo-EM 3D Density maps.
Article in Proceedings. IEEE International Conference on Bioinformatics and Biomedicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Multi-plane attention guided 3D nnU-Net for MRI-based cervical cancer segmentation.Frontiers in oncology · 2026Article
- CervSpineNet: a hybrid deep learning-based approach for the segmentation of cervical spinous processes.Frontiers in bioengineering and biotechnology · 2025Article
- The combined focal loss and dice loss function improves the segmentation of beta-sheets in medium-resolution cryo-electron-microscopy density maps.Bioinformatics advances · 2024Article
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Abstract
Although cryo-electron microscopy (cryo-EM) has been successfully used to derive atomic structures for many proteins, it is still challenging to derive atomic structure when the resolution of cryo-EM density maps is in the medium resolution range such as 5-10 Å. Although multiple neural networks have been proposed for the problem of secondary structure detection from cryo-EM 3D images, loss functions used in the existing networks are primarily based on cross entropy loss (CE). In order to study the behavior of various loss functions in the secondary structure detection problem, we investigated five loss functions and compared their performances. Using a U-net architecture in DeepSSETracer and a test set of 65 protein chains of atomic structures and their corresponding cryo-EM density component maps, we found that the combined function with focal cross entropy loss (FCE) and Dice loss (DL) provides the best overall detection of secondary structures. In particular, the combined loss function has a significant enhancement of an overall F
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