Phase retrieval for X-ray differential phase contrast radiography with knowledge transfer learning from virtual differential absorption model

Tao, Siwei; Tian, Zonghan; Bai, Ling; Xu, Yueshu; Kuang, Cuifang; Liu, Xu

doi:10.1016/j.compbiomed.2023.107711

Computers in Biology and Medicine

2024

DOI: 10.1016/j.compbiomed.2023.107711

|View full text |Cite

Phase retrieval for X-ray differential phase contrast radiography with knowledge transfer learning from virtual differential absorption model

Siwei Tao,

Zonghan Tian,

Ling Bai

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 Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Enhanced tissue slide imaging in the complex domain via cross-explainable GAN for Fourier ptychographic microscopy

Bardozzo,

Fiore,

Valentino

et al. 2024

Computers in Biology and Medicine

View full text Add to dashboard Cite

Enhanced tissue slide imaging in the complex domain via cross-explainable GAN for Fourier ptychographic microscopy

Bardozzo,

Fiore,

Valentino

et al. 2024

Computers in Biology and Medicine

View full text Add to dashboard Cite

Identification of kidney stones in KUB X-ray images using VGG16 empowered with explainable artificial intelligence

Ahmed,

Abbas,

Athar

et al. 2024

Sci Rep

View full text Add to dashboard Cite

A kidney stone is a solid formation that can lead to kidney failure, severe pain, and reduced quality of life from urinary system blockages. While medical experts can interpret kidney-ureter-bladder (KUB) X-ray images, specific images pose challenges for human detection, requiring significant analysis time. Consequently, developing a detection system becomes crucial for accurately classifying KUB X-ray images. This article applies a transfer learning (TL) model with a pre-trained VGG16 empowered with explainable artificial intelligence (XAI) to establish a system that takes KUB X-ray images and accurately categorizes them as kidney stones or normal cases. The findings demonstrate that the model achieves a testing accuracy of 97.41% in identifying kidney stones or normal KUB X-rays in the dataset used. VGG16 model delivers highly accurate predictions but lacks fairness and explainability in their decision-making process. This study incorporates the Layer-Wise Relevance Propagation (LRP) technique, an explainable artificial intelligence (XAI) technique, to enhance the transparency and effectiveness of the model to address this concern. The XAI technique, specifically LRP, increases the model's fairness and transparency, facilitating human comprehension of the predictions. Consequently, XAI can play an important role in assisting doctors with the accurate identification of kidney stones, thereby facilitating the execution of effective treatment strategies.

show abstract

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.

Phase retrieval for X-ray differential phase contrast radiography with knowledge transfer learning from virtual differential absorption model

Cited by 2 publications

References 42 publications

Enhanced tissue slide imaging in the complex domain via cross-explainable GAN for Fourier ptychographic microscopy

Enhanced tissue slide imaging in the complex domain via cross-explainable GAN for Fourier ptychographic microscopy

Identification of kidney stones in KUB X-ray images using VGG16 empowered with explainable artificial intelligence

Contact Info

Product

Resources

About