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Collage CNN for Renal Cell Carcinoma Detection from CT

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Machine Learning in Medical Imaging (MLMI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10541))

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Abstract

Renal cell carcinoma (RCC) is a common malignancy that accounts for a steadily increasing mortality rate worldwide. Widespread use of abdominal imaging in recent years, mainly CT and MRI, has significantly increased the detection rates of such cancers. However, detection still relies on a laborious manual process based on visual inspection of 2D image slices. In this paper, we propose an image collage based deep convolutional neural network (CNN) approach for automatic detection of pathological kidneys containing RCC. Our collage approach overcomes the absence of slice-wise training labels, enables slice-reshuffling based data augmentation, and offers favourable training time and performance compared to 3D CNNs. When validated on clinical CT datasets of 160 patients from the TCIA database, our method classified RCC cases vs. normal kidneys with 98% accuracy.

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Acknowledgement

This work is supported in part by the Institute for Computing, Information and Cognitive Systems (ICICS) at UBC.

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Authors and Affiliations

  1. BiSICL, University of British Columbia, Vancouver, BC, Canada

    Mohammad Arafat Hussain, Alborz Amir-Khalili & Rafeef Abugharbieh

  2. Medical Image Analysis Lab, Simon Fraser University, Burnaby, BC, Canada

    Ghassan Hamarneh

Authors
  1. Mohammad Arafat Hussain
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  2. Alborz Amir-Khalili
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  3. Ghassan Hamarneh
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  4. Rafeef Abugharbieh
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Corresponding author

Correspondence to Mohammad Arafat Hussain .

Editor information

Editors and Affiliations

  1. Shanghai Jiao Tong University, Shanghai, China

    Qian Wang

  2. Nanjing University , Nanjing, China

    Yinghuan Shi

  3. Korea University , Seoul, Korea (Republic of)

    Heung-Il Suk

  4. Illinois Institute of Technology, Chicago, Illinois, USA

    Kenji Suzuki

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Hussain, M.A., Amir-Khalili, A., Hamarneh, G., Abugharbieh, R. (2017). Collage CNN for Renal Cell Carcinoma Detection from CT. In: Wang, Q., Shi, Y., Suk, HI., Suzuki, K. (eds) Machine Learning in Medical Imaging. MLMI 2017. Lecture Notes in Computer Science(), vol 10541. Springer, Cham. https://doi.org/10.1007/978-3-319-67389-9_27

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  • DOI: https://doi.org/10.1007/978-3-319-67389-9_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67388-2

  • Online ISBN: 978-3-319-67389-9

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