ArticleAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference2014
Semantic interpretation of robust imaging features for Fuhrman grading of renal carcinoma.
Article in Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Empowering Renal Cancer Management with AI and Digital Pathology: Pathology, Diagnostics and Prognosis.Biomedicines · 2023Review
- Histopathology Feature Mining and Association with Hyperspectral Imaging for the Detection of Squamous Neoplasia.Scientific reports · 2019Article
- A Minimum Spanning Forest-Based Method for Noninvasive Cancer Detection With Hyperspectral Imaging.IEEE transactions on bio-medical engineering · 2016Article
- Quantitative Diagnosis of Tongue Cancer from Histological Images in an Animal Model.Proceedings of SPIE--the International Society for Optical Engineering · 2016Article
- Quantitative Wavelength Analysis and Image Classification for Intraoperative Cancer Diagnosis with Hyperspectral Imaging.Proceedings of SPIE--the International Society for Optical Engineering · 2015Article
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Authors and funding
6 authors.
Funding
Abstract
Pattern recognition in tissue biopsy images can assist in clinical diagnosis and identify relevant image characteristics linked with various biological characteristics. Although previous work suggests several informative imaging features for pattern recognition, there exists a semantic gap between characteristics of these features and pathologists' interpretation of histopathological images. To address this challenge, we develop a clinical decision support system for automated Fuhrman grading of renal carcinoma biopsy images. We extract 1316 color, shape, texture and topology features and develop one vs. all models for four Fuhrman grades. Our models are highly accurate with 90.4% accuracy in a four-class prediction. Predictivity analysis suggests good generalization of the model development methodology through robustness to dataset sampling in cross-validation. We provide a semantic interpretation for the imaging features used in these models by linking features to pathologists' grading criteria. Our study identifies novel imaging features that are semantically linked to Fuhrman grading criteria.
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