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Combining Supervised and Unsupervised Methods to Support Early Diagnosis of Hepatocellular Carcinoma

  • Federica Ciocchetta
  • Rossana Dell’Anna
  • Francesca Demichelis
  • Amar Paul Dhillon
  • Alberto Quaglia
  • Andrea Sboner
Conference paper
  • 449 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2780)

Abstract

The early diagnosis of Hepatocellular Carcinoma (HCC) is extremely important for effective treatment and improvements in diagnosis are indispensable, particularly concerning the differentiation between “early” HCC and non neoplastic nodules. In this paper, we reconsidered the results obtained previously and compared them with the results of an unsupervised method to achieve a deep knowledge on uncertain lesions. This analysis agreed with the predictions on DNs obtained by the supervised system, providing pathologists with reliable information to support their diagnostic process.

Keywords

Hepatocellular Carcinoma Conjunctive Normal Form Feature Selection Algorithm Unsupervised Method Dysplastic Nodule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Federica Ciocchetta
    • 1
  • Rossana Dell’Anna
    • 1
  • Francesca Demichelis
    • 1
  • Amar Paul Dhillon
    • 2
  • Alberto Quaglia
    • 2
  • Andrea Sboner
    • 1
  1. 1.ITC-irstPovo (TN)Italy
  2. 2.Royal Free and University College Medical SchoolHampstead, LondonUK

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