WikiSysop: Created page with "== Citation == Fang, Kun / Wang, JinLing / Chen, QingFeng / Feng, Xian / Qu, YouMing / Shi, Jiachi / Xu, Zhuomin. Swin-cryoEM: Multi-class cryo-electron micrographs single particle mixed detection method. 2024. Plos one, Vol. 19, No. 4, p. e0298287 == Abstract == Cryo-electron micrograph images have various characteristics such as varying sizes, shapes, and distribution densities of individual particles, severe background noise, high levels of impurities, irregular sh..."

2024-08-08T06:35:12Z

Created page with "== Citation == Fang, Kun / Wang, JinLing / Chen, QingFeng / Feng, Xian / Qu, YouMing / Shi, Jiachi / Xu, Zhuomin. Swin-cryoEM: Multi-class cryo-electron micrographs single particle mixed detection method. 2024. Plos one, Vol. 19, No. 4, p. e0298287 == Abstract == Cryo-electron micrograph images have various characteristics such as varying sizes, shapes, and distribution densities of individual particles, severe background noise, high levels of impurities, irregular sh..."

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== Citation ==

Fang, Kun / Wang, JinLing / Chen, QingFeng / Feng, Xian / Qu, YouMing / Shi, Jiachi / Xu, Zhuomin. Swin-cryoEM: Multi-class cryo-electron micrographs single particle mixed detection method. 2024. Plos one, Vol. 19, No. 4, p. e0298287

== Abstract ==

Cryo-electron micrograph images have various characteristics such as varying sizes,
shapes, and distribution densities of individual particles, severe background noise, high levels
of impurities, irregular shapes, blurred edges, and similar color to the background. How
to demonstrate good adaptability in the field of image vision by picking up single particles
from multiple types of cryo-electron micrographs is currently a challenge in the field of cryoelectron
micrographs. This paper combines the characteristics of the MixUp hybrid
enhancement algorithm, enhances the image feature information in the pre-processing
stage, builds a feature perception network based on the channel self-attention mechanism
in the forward network of the Swin Transformer model network, achieving adaptive adjustment
of self-attention mechanism between different single particles, increasing the network’s
tolerance to noise, Incorporating PReLU activation function to enhance information
exchange between pixel blocks of different single particles, and combining the Cross-
Entropy function with the softmax function to construct a classification network based on
Swin Transformer suitable for cryo-electron micrograph single particle detection model
(Swin-cryoEM), achieving mixed detection of multiple types of single particles. Swin-cryoEM
algorithm can better solve the problem of good adaptability in picking single particles of
many types of cryo-electron micrographs, improve the accuracy and generalization ability of
the single particle picking method, and provide high-quality data support for the three-dimensional
reconstruction of a single particle. In this paper, ablation experiments and comparison
experiments were designed to evaluate and compare Swin-cryoEM algorithms in detail and
comprehensively on multiple datasets. The Average Precision is an important evaluation
index of the evaluation model, and the optimal Average Precision reached 95.5% in the
training stage Swin-cryoEM, and the single particle picking performance was also superior
in the prediction stage. This model inherits the advantages of the Swin Transformer detection
model and is superior to mainstream models such as Faster R-CNN and YOLOv5 in
terms of the single particle detection capability of cryo-electron micrographs.

== Keywords ==

== Links ==

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0298287

== Related software ==

== Related methods ==

== Comments ==

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