title: Clinically-inspired automatic classification of ovarian carcinoma subtypes.
abstract: BACKGROUND
  It has been shown that ovarian carcinoma subtypes are distinct pathologic entities with differing prognostic and therapeutic implications. Histotyping by pathologists has good reproducibility, but occasional cases are challenging and require immunohistochemistry and subspecialty consultation. Motivated by the need for more accurate and reproducible diagnoses and to facilitate pathologists' workflow, we propose an automatic framework for ovarian carcinoma classification.
  
  
  METHODS
  Our method is inspired by pathologists' workflow. We analyse imaged tissues at two magnification levels and extract clinically-inspired color, texture, and segmentation-based shape descriptors using image-processing methods. We propose a carefully designed machine learning technique composed of four modules: A dissimilarity matrix, dimensionality reduction, feature selection and a support vector machine classifier to separate the five ovarian carcinoma subtypes using the extracted features.
  
  
  RESULTS
  This paper presents the details of our implementation and its validation on a clinically derived dataset of eighty high-resolution histopathology images. The proposed system achieved a multiclass classification accuracy of 95.0% when classifying unseen tissues. Assessment of the classifier's confusion (confusion matrix) between the five different ovarian carcinoma subtypes agrees with clinician's confusion and reflects the difficulty in diagnosing endometrioid and serous carcinomas.
  
  
  CONCLUSIONS
  Our results from this first study highlight the difficulty of ovarian carcinoma diagnosis which originate from the intrinsic class-imbalance observed among subtypes and suggest that the automatic analysis of ovarian carcinoma subtypes could be valuable to clinician's diagnostic procedure by providing a second opinion.
authors: Department of Computing Sciences, Medical Image Analysis Lab, Simon Fraser University, Burnaby, Canada.
authors: Aïcha
authors: A
authors: BenTaieb
authors: Department of Computing Sciences, Medical Image Analysis Lab, Simon Fraser University, Burnaby, Canada.
authors: Masoud S
authors: MS
authors: Nosrati
authors: Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada.
authors: Hector
authors: H
authors: Li-Chang
authors: Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada.
authors: David
authors: D
authors: Huntsman
authors: Department of Computing Sciences, Medical Image Analysis Lab, Simon Fraser University, Burnaby, Canada.
authors: Ghassan
authors: G
authors: Hamarneh
keywords: Computer-aided diagnosis; machine learning; ovarian carcinoma