@article{EuniceCretu2008_22,
  title={Translational approach for semi-automatic breast cancer grading using a knowledge-guided semantic indexing of histopathology images},
  pub_year={2008},
  citation={Proc. MIAAB, 1-8, 2008},
  author={Adina Eunice Tutac and Daniel Racoceanu and Wee-Kheng Leow and Jean Romain Dalle and Thomas Putti and Wei Xiong and Vladimir Cretu},
  journal={Proc. MIAAB},
  pages={1-8},
  abstract={Within the last decade, histological grading has become widely accepted as a powerful indicator of prognosis in breast cancer. Currently, Breast Cancer Grading (BCG) is achieved by pathologists using tedious subjective visual examinations of hundred of slices day. In order to eliminate these drawbacks, we propose a semi-automatic grading system in a structured semantic Content-Based Image Retrieval (CBIR) framework. Although considered as an encouraging technology to enhance the intrinsic functionality of managing medical images, CBIR faces various issues with respect to clinical applications. One of these problems is the content gap, conceptually consisting of two major gaps: the semantic gap, defined as the discrepancy between the low-level visual features and high-level semantic concepts-and the context gap, which refers to the limitation of CBIR usage to a specific context. To tackle with these issues, this paper introduces two approaches, related to the semiautomatic breast cancer grading challenge: on one hand, a medical knowledge guided paradigm for semantic indexing of histopathology images, to overcome the semantic gap, and on the other hand, we propose a semiautomatic BCG approach, in order to improve pathologists’ current manual procedures biased by subjectivity and tedious factors. The key idea is to build a Web Ontology Language standards compliant semiautomatic translation framework, from the medical concepts/rules related to the BCG, to computer vision (CV) concepts/symbolic rules. The application is related to a generic framework for BCG which narrows the context gap. This approach was tested over …}
}