Comput. Biol. Med. - Segmentation of histopathology HER2/neu images with fuzzy decision tree and Takagi-Sugeno reasoning.

Tópicos

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Resumo

The Human Epidermal Growth Factor Receptor 2 (HER2/neu) is a biomarker, recognized as a valuable prognostic and predictive factor for breast cancer. In approximately 20% of primary breast cancers, the HER2/neu protein is over-expressed. By recent clinical research, a treatment procedure, with corresponding monoclonal antibodies specifically designed to target the HER2/neu receptor, was confirmed. Therefore, in modern breast cancer diagnostics, it is critical to provide accurate recognition of the HER2/neu positive breast cancer. This can be done by segmentation of the membranes of cancer cells that are visualized as HER2/neu over-expressed on images acquired from corresponding histopathology preparations. In our research, we propose an accurate segmentation process of these structures using an appropriately defined fuzzy decision tree. Moreover, we introduce a new reasoning concept based on the Takagi-Sugeno inference model.

Resumo Limpo

human epiderm growth factor receptor herneu biomark recogn valuabl prognost predict factor breast cancer approxim primari breast cancer herneu protein overexpress recent clinic research treatment procedur correspond monoclon antibodi specif design target herneu receptor confirm therefor modern breast cancer diagnost critic provid accur recognit herneu posit breast cancer can done segment membran cancer cell visual herneu overexpress imag acquir correspond histopatholog prepar research propos accur segment process structur use appropri defin fuzzi decis tree moreov introduc new reason concept base takagisugeno infer model

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