Abstract
Pathologists often deal with high complexity and sometimes disagreement over Osteosarcoma tumor classification due to cellular heterogeneity in the dataset. Segmentation and classification of histology tissue in H&E stained tumor image datasets is challenging due to intra-class variations and inter-class similarity, crowded context, and noisy data. In recent years, deep learning approaches have led to encouraging results in breast cancer and prostate cancer analysis. In this paper, we propose a Convolutional neural network (CNN) as a tool to improve efficiency and accuracy of Osteosarcoma tumor classification into tumor classes (viable tumor, necrosis) vs non-tumor. The proposed CNN architecture contains five learned layers: three convolutional layers interspersed with max pooling layers for feature extraction and two fully-connected layers with data augmentation strategies to boost performance. We conclude that the use of neural network can assure high accuracy and efficiency in Osteosarcoma classification.
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Acknowledgement
This research was partially supported by NSF award IIP14 39718 and CPRIT award RP150164. We would like to thank Harish Arunchalam, Bodgan Armaselu and Dr. Riccardo Ziraldo from our group at UT Dallas, and Dr. Lan Ma, University of Maryland, for their helpful discussions. We also would like to thank John-Paul Bach and Sammy Glick from UT Southwestern Medical Center for their help with the datasets.
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Mishra, R., Daescu, O., Leavey, P., Rakheja, D., Sengupta, A. (2017). Histopathological Diagnosis for Viable and Non-viable Tumor Prediction for Osteosarcoma Using Convolutional Neural Network. In: Cai, Z., Daescu, O., Li, M. (eds) Bioinformatics Research and Applications. ISBRA 2017. Lecture Notes in Computer Science(), vol 10330. Springer, Cham. https://doi.org/10.1007/978-3-319-59575-7_2
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