An automatic classification method of testicular histopathology based on SC-YOLO framework.

IF 2.2 4区 工程技术 Q3 BIOCHEMICAL RESEARCH METHODS
Jinggen Wu,Yao Sun,Yangbo Jiang,Yangcheng Bu,Chong Chen,Jingping Li,Lejun Li,Weikang Chen,Keren Cheng,Jian Xu
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引用次数: 0

Abstract

The pathological diagnosis and treatment of azoospermia depend on precise identification of spermatogenic cells. Traditional methods are time-consuming and highly subjective due to complexity of Johnsen score, posing challenges for accurately diagnosing azoospermia. Here, we introduce a novel SC-YOLO framework for automating the classification of spermatogenic cells that integrates S3Ghost module, CoordAtt module and DCNv2 module, effectively capturing texture and shape features of spermatogenic cells while reducing model parameters. Furthermore, we propose a simplified Johnsen score criteria to expedite the diagnostic process. Our SC-YOLO framework presents the higher efficiency and accuracy of deep learning technology in spermatogenic cell recognition. Future research endeavors will focus on optimizing the model's performance and exploring its potential for clinical applications.
基于 SC-YOLO 框架的睾丸组织病理学自动分类方法。
无精子症的病理诊断和治疗取决于对生精细胞的精确鉴定。传统方法耗时长,而且由于约翰森评分的复杂性,主观性很强,给无精子症的准确诊断带来了挑战。在此,我们介绍了一种用于自动分类生精细胞的新型 SC-YOLO 框架,该框架集成了 S3Ghost 模块、CoordAtt 模块和 DCNv2 模块,可有效捕捉生精细胞的纹理和形状特征,同时减少模型参数。此外,我们还提出了简化的约翰森评分标准,以加快诊断过程。我们的 SC-YOLO 框架展示了深度学习技术在生精细胞识别中的更高效率和准确性。未来的研究工作将侧重于优化模型的性能,并探索其在临床应用中的潜力。
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来源期刊
BioTechniques
BioTechniques 工程技术-生化研究方法
CiteScore
4.40
自引率
0.00%
发文量
68
审稿时长
3.3 months
期刊介绍: BioTechniques is a peer-reviewed, open-access journal dedicated to publishing original laboratory methods, related technical and software tools, and methods-oriented review articles that are of broad interest to professional life scientists, as well as to scientists from other disciplines (e.g., chemistry, physics, computer science, plant and agricultural science and climate science) interested in life science applications for their technologies. Since 1983, BioTechniques has been a leading peer-reviewed journal for methods-related research. The journal considers: Reports describing innovative new methods, platforms and software, substantive modifications to existing methods, or innovative applications of existing methods, techniques & tools to new models or scientific questions Descriptions of technical tools that facilitate the design or performance of experiments or data analysis, such as software and simple laboratory devices Surveys of technical approaches related to broad fields of research Reviews discussing advancements in techniques and methods related to broad fields of research Letters to the Editor and Expert Opinions highlighting interesting observations or cautionary tales concerning experimental design, methodology or analysis.
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