Abstract
This paper presents a novel automated tumor segmentation approach for Hematoxylin & Eosin stained histology images. The proposed method enhances the segmentation performance by combining the topological and convolution neural network (CNN) features. Our approach is based on 3 steps: (1) construct enhanced persistent homology profiles by using topological features; (2) train a CNN to extract convolutional features; (3) employ a multi-stage ensemble strategy to combine Random Forest regression models. The experimental results demonstrate that proposed method outperforms the conventional CNN.
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Acknowledgments
The first author (Qaiser) acknowledges the financial support provided by the University Hospital Coventry Warwickshire (UHCW) and the Department of Computer Science at the University of Warwick.
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Qaiser, T., Tsang, YW., Epstein, D., Rajpoot, N. (2017). Tumor Segmentation in Whole Slide Images Using Persistent Homology and Deep Convolutional Features. In: Valdés Hernández, M., González-Castro, V. (eds) Medical Image Understanding and Analysis. MIUA 2017. Communications in Computer and Information Science, vol 723. Springer, Cham. https://doi.org/10.1007/978-3-319-60964-5_28
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DOI: https://doi.org/10.1007/978-3-319-60964-5_28
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