title: An analysis of scale and rotation invariance in the bag-of-features method for histopathological image classification.
abstract: The bag-of-features method has emerged as a useful and flexible tool that can capture medically relevant image characteristics. In this paper, we study the effect of scale and rotation invariance in the bag-of-features framework for Renal Cell Carcinoma subtype classification. We estimated the performance of different features by linear support vector machine over 10 iterations of 3-fold cross validation. For a very heterogeneous dataset labeled by an expert pathologist, we achieve a classification accuracy of 88% with four subtypes. Our study shows that rotation invariance is more important than scale invariance but combining both properties gives better classification performance.
authors: School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
authors: S Hussain
authors: SH
authors: Raza
authors: R Mitchell
authors: RM
authors: Parry
authors: Richard A
authors: RA
authors: Moffitt
authors: Andrew N
authors: AN
authors: Young
authors: May D
authors: MD
authors: Wang
mesh_terms: D000465:Algorithms; D002292:Carcinoma, Renal Cell / Q000175:diagnosis* / Q000473:pathology; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D003952:Diagnostic Imaging / Q000379:methods; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D007680:Kidney Neoplasms / Q000175:diagnosis* / Q000473:pathology; D008279:Magnetic Resonance Imaging / Q000379:methods; D008495:Medical Oncology / Q000379:methods; D015233:Models, Statistical; D015203:Reproducibility of Results; D014057:Tomography, X-Ray Computed / Q000379:methods