Histopathological diagnosis for viable and non-viable tumor prediction for osteosarcoma using convolutional neural network

Rashika Mishra, Ovidiu Daescu, Patrick J Leavey, Dinesh Rakheja, Anita L Sengupta

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationBioinformatics Research and Applications - 13th International Symposium, ISBRA 2017, Proceedings
PublisherSpringer Verlag
Pages12-23
Number of pages12
Volume10330 LNBI
ISBN (Print)9783319595740
DOIs
StatePublished - 2017
Event13th International Symposium on Bioinformatics Research and Applications, ISBRA 2017 - Honolulu, United States
Duration: May 29 2017Jun 2 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10330 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other13th International Symposium on Bioinformatics Research and Applications, ISBRA 2017
CountryUnited States
CityHonolulu
Period5/29/176/2/17

Keywords

  • Convolutional neural network
  • Histology image analysis
  • Osteosarcoma

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Histopathological diagnosis for viable and non-viable tumor prediction for osteosarcoma using convolutional neural network'. Together they form a unique fingerprint.

  • Cite this

    Mishra, R., Daescu, O., Leavey, P. J., Rakheja, D., & Sengupta, A. L. (2017). Histopathological diagnosis for viable and non-viable tumor prediction for osteosarcoma using convolutional neural network. In Bioinformatics Research and Applications - 13th International Symposium, ISBRA 2017, Proceedings (Vol. 10330 LNBI, pp. 12-23). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10330 LNBI). Springer Verlag. https://doi.org/10.1007/978-3-319-59575-7_2