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International MICCAI Workshop on Medical Computer Vision

MCV 2013: Medical Computer Vision. Large Data in Medical Imaging pp 161–174Cite as

Computer Aided Diagnosis Using Multilevel Image Features on Large-Scale Evaluation

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8331))

Abstract

Computer aided diagnosis (CAD) of cancerous anatomical structures via 3D medical images has emerged as an intensively studied research area. In this paper, we present a principled three-tiered image feature learning approach to capture task specific and data-driven class discriminative statistics from an annotated image database. It integrates voxel-, instance-, and database-level feature learning, aggregation and parsing. The initial segmentation is proceeded as robust voxel labeling and thresholding. After instance-level spatial aggregation, extracted features can also be flexibly tuned for classifying lesions, or discriminating different subcategories of lesions. We demonstrate the effectiveness in the lung nodule detection task which handles all types of solid, partial-solid, and ground-glass nodules using the same set of learned features. Our hierarchical feature learning framework, which was extensively trained and validated on large-scale multiple site datasets of \(879\) CT volumes (510 training and 369 validation), achieves superior performance than other state-of-the-art CAD systems. The proposed method is also shown to be applicable for colonic polyp detection, including all polyp morphological subcategories, via 770 tagged-prep CT scans from multiple medical sites (358 training and 412 validation).

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Notes

  1. 1.

    PBT is a powerful two-class and multiclass discriminative learning framework [20]. Random Forests or Ferns [21, 22] are also applicable for training voxel-level labeler. Our empirical experience shows that PBT can learn very similar or slightly better ROC curves with much simpler model complexity, i.e., one tree versus multiple trees per model/classifier.

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Author information

Authors and Affiliations

  1. National Institutes of Health, Bethesda, USA

    Le Lu

  2. Siemens Medical Solutions USA, Malvern, USA

    Pandu Devarakota, Siddharth Vikal & Matthias Wolf

  3. Siemens Corporate Research, Princeton, USA

    Le Lu, Dijia Wu & Yefeng Zheng

  4. Microsoft Corporation, Redmond, USA

    Dijia Wu

Authors
  1. Le Lu
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  2. Pandu Devarakota
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  3. Siddharth Vikal
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  4. Dijia Wu
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  5. Yefeng Zheng
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  6. Matthias Wolf
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Corresponding author

Correspondence to Le Lu .

Editor information

Editors and Affiliations

  1. Technical University of Munich, ETH, Zürich, Switzerland, Munich, Germany

    Bjoern Menze

  2. Medical University of Vienna, Vienna, Austria

    Georg Langs

  3. GE Global Research, Niskayuna, New York, USA

    Albert Montillo

  4. Siemens AG, Erlangen, Germany

    Michael Kelm

  5. University of Applied Sciences Western Switzerland, Sierre, Switzerland

    Henning Müller

  6. University of California, La Jolla, California, USA

    Zhuowen Tu

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Lu, L., Devarakota, P., Vikal, S., Wu, D., Zheng, Y., Wolf, M. (2014). Computer Aided Diagnosis Using Multilevel Image Features on Large-Scale Evaluation. In: Menze, B., Langs, G., Montillo, A., Kelm, M., Müller, H., Tu, Z. (eds) Medical Computer Vision. Large Data in Medical Imaging. MCV 2013. Lecture Notes in Computer Science(), vol 8331. Springer, Cham. https://doi.org/10.1007/978-3-319-05530-5_16

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  • DOI: https://doi.org/10.1007/978-3-319-05530-5_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-05529-9

  • Online ISBN: 978-3-319-05530-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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