DEST: Deep Enhanced Swin Transformer Toward Better Scoring for NAFLD

Yan, Renao; He, Qiming; Liu, Yiqing; Gou, Jizhou; Sun, Qiehe; Zhou, Guangde; He, Yonghong; Guan, Tian

doi:10.1007/978-3-031-18910-4_17

Pattern Recognition and Computer Vision

2022

DOI: 10.1007/978-3-031-18910-4_17

|View full text |Cite

DEST: Deep Enhanced Swin Transformer Toward Better Scoring for NAFLD

Renao Yan

Qiming He

Yiqing Liu

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article2

Relationship

Self Cite1

Independent1

Authors

Journals

Cited by 2 publications

(1 citation statement)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Deep learning has demonstrated great application value in several medical fields. [9][10][11] Recently, it has achieved comparable performance in pathological image analysis, including classification, 12 segmentation, 13 detection, 14 and assisted diagnosis. [15][16][17] Furthermore, deep learning is increasingly playing on pathological advanced tasks, including gene prediction, 18,19 survival analysis, 20,21 virtual staining, 22 etc.…”

mentioning

confidence: 99%

Identifying and matching 12‐level multistained glomeruli via deep learning for diagnosis of glomerular diseases

He,

Zeng,

et al. 2024

Int J Imaging Syst Tech

Self Cite

View full text Add to dashboard Cite

The assessment of glomerular lesions is a fundamental step toward the diagnosis of glomerular diseases. This requires diagnosis and fusion of information from all the glomeruli at multiple levels and stainings. The lack of research on multi‐level multistained glomerular identification and matching has resulted in renal pathologists devoting much time and attention to this time‐consuming and labor‐intensive process. This limits the overall efficiency of the diagnosis of glomerular diseases. This paper constructed a dataset consisting of 600 cases, each containing 12 levels of whole slide images from H&E, PAS, Masson trichrome, and PASM staining. The glomeruli identifying and matching was proposed. First, a multistained transformer‐based Mask R‐CNN is proposed to extract the position and contours of the glomeruli. Second, coherent point drift‐based coarse matching and hybrid feature‐based fine matching achieve pairwise matching. Finally, the voting‐based cross‐matching realizes 12‐level multistained matching. This system constitutes a practical human‐computer interface. Intensive experiments were conducted to validate the ability to identify and match 12‐level multistained glomeruli. The mAP@50 for detection and segmentation reached 95.40% and 95.70%, respectively. The basic and comprehensive matching rates of the glomeruli matching reached 98.25% and 74.59%, respectively. Visualization results further demonstrate that the model achieved accurate identification and matching. The proposed system achieves accurate identification and matching of 12 levels of multistained glomeruli and can serve as a tool for pathological diagnosis of glomerular diseases by simplifying the diagnostic process. More importantly, this system can lay the foundation for the fully automated assisted diagnosis of glomerular diseases.

show abstract

mentioning

confidence: 99%

Identifying and matching 12‐level multistained glomeruli via deep learning for diagnosis of glomerular diseases

He,

Zeng,

et al. 2024

Int J Imaging Syst Tech

Self Cite

View full text Add to dashboard Cite

show abstract

Global attention based GNN with Bayesian collaborative learning for glomerular lesion recognition

He,

Ge,

Zeng

et al. 2024

Computers in Biology and Medicine

View full text Add to dashboard Cite

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

DEST: Deep Enhanced Swin Transformer Toward Better Scoring for NAFLD

Cited by 2 publications

References 20 publications

Identifying and matching 12‐level multistained glomeruli via deep learning for diagnosis of glomerular diseases

Identifying and matching 12‐level multistained glomeruli via deep learning for diagnosis of glomerular diseases

Global attention based GNN with Bayesian collaborative learning for glomerular lesion recognition

Contact Info

Product

Resources

About