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SpineNet: Automatically Pinpointing Classification Evidence in Spinal MRIs

  • Amir JamaludinEmail author
  • Timor Kadir
  • Andrew Zisserman
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9901)

Abstract

We describe a method to automatically predict radiological scores in spinal Magnetic Resonance Images (MRIs). Furthermore, we also identify and localize the pathologies that are the reasons for these scores. We term these pathological regions the “evidence hotspots”. Our contributions are two-fold: (i) a Convolutional Neural Network (CNN) architecture and training scheme to predict multiple radiological scores on multiple slice sagittal MRIs. The scheme uses multi-task CNN training with augmentation, and handles the class imbalance common in medical classification tasks. (ii) the prediction of a heat-map of evidence hotspots for each score. For both of these, all that is required for training is the class label of the disc or vertebrae, no stronger supervision (such as slice labels) is needed. We report state-of-the-art and near-human performances across multiple radiological scorings including: Pfirrmann grading, disc narrowing, endplate defects, and marrow changes.

Keywords

Convolutional Neural Network Class Imbalance Convolutional Layer Radiological Score Pfirrmann Grade 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

We are grateful for discussions with Prof. Jeremy Fairbank and Dr. Jill Urban, and Prof. Iain McCall for the radiological scores. This work was supported by the RCUK CDT in Healthcare Innovation (EP/G036861/1) and the EPSRC Programme Grant Seebibyte (EP/M013774/1). The data was obtained during the EC FP7 project (HEALTH-F2-2008-201626).

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.University of OxfordOxfordUK
  2. 2.Mirada MedicalOxfordUK

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