Machine Learning on Biomedical Images: Interactive Learning, Transfer Learning, Class Imbalance, and Beyond

02/13/2019 ∙ by Naimul Mefraz Khan, et al. ∙ Ryerson University 0

In this paper, we highlight three issues that limit performance of machine learning on biomedical images, and tackle them through 3 case studies: 1) Interactive Machine Learning (IML): we show how IML can drastically improve exploration time and quality of direct volume rendering. 2) transfer learning: we show how transfer learning along with intelligent pre-processing can result in better Alzheimer's diagnosis using a much smaller training set 3) data imbalance: we show how our novel focal Tversky loss function can provide better segmentation results taking into account the imbalanced nature of segmentation datasets. The case studies are accompanied by in-depth analytical discussion of results with possible future directions.



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  • [1] N. Abraham and N. M. Khan. A novel focal tversky loss function with improved attention u-net for segmentation. In IEEE Symposium on Biomedical Imaging, 2019.
  • [2] A. Gupta, M. Ayhan, and A. Maida. Natural image bases to represent neuroimaging data. In International Conference on Machine Learning, 2013.
  • [3] S. R. Hashemi et al. Tversky as a loss function for highly unbalanced image segmentation using 3d fully convolutional deep networks. CoRR, abs/1803.11078, 2018.
  • [4] M. Hon and N. M. Khan. Towards alzheimer’s disease classification through transfer learning. In IEEE International Conference on Bioinformatics and Biomedicine, 2017.
  • [5] Hosseini-Asl et al. Alzheimer’s disease diagnostics by adaptation of 3d convolutional network. In IEEE International Conference Image Processing, 2016.
  • [6] C. R. Jack et al. The alzheimer’s disease neuroimaging initiative (adni): Mri methods. Journal of magnetic resonance imaging, 27(4):685–691, 2008.
  • [7] N. M. Khan, M. Kyan, and L. Guan. Intuitive volume exploration through spherical self-organizing map and color harmonization. Neurocomputing, 147:160–173, 2015.
  • [8] B. Kim. Interactive and interpretable machine learning models for human machine collaboration. PhD thesis, Massachusetts Institute of Technology, 2015.
  • [9] G. Kindlmann and J. Durkin. Semi-automatic generation of transfer functions for direct volume rendering. In IEEE symposium on Volume visualization, 1998.
  • [10] S. Klöppel et al. Accuracy of dementia diagnosis - a direct comparison between radiologists and a computerized method. Brain, 131(11):2969–2974, 2008.
  • [11] T. Kohonen, editor. Self-organizing maps. Springer-Verlag New York, Inc., Secaucus, NJ, USA, 1997.
  • [12] B. C. Kwon et al.

    Retainvis: Visual analytics with interpretable and interactive recurrent neural networks on electronic medical records.

    IEEE transactions on visualization and computer graphics, 25(1):299–309, 2019.
  • [13] T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár. Focal loss for dense object detection. IEEE transactions on pattern analysis and machine intelligence, 2018.
  • [14] Y. Liu, C. Lisle, and J. Collins. Quick2insight: A user-friendly framework for interactive rendering of biological image volumes. In

    IEEE Symposium on Biological Data Visualization

    . IEEE Press, 2011.
  • [15] R. Maciejewski, I. Wu, W. Chen, , and D. Ebert. Structuring feature space: A non-parametric method for volumetric transfer function generation. IEEE Transactions on Visualization and Computer Graphics, 15:1473–1480, 2009.
  • [16] B. Nguyen et al. A clustering-based system to automate transfer function design for medical image visualization. The Visual Computer, 28:181–191, 2012.
  • [17] O. Oktay et al. Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999, 2018.
  • [18] C. Plant et al. Automated detection of brain atrophy patterns based on mri for the prediction of alzheimer’s disease. Neuroimage, 50(1):162–174, 2010.
  • [19] S. Roettger. The volume library. 2012.
  • [20] S. Roettger, M. Bauer, and M. Stamminger. Spatialized transfer functions. In IEEE/Eurographics symposium on visualization. IEEE Press, 2005.
  • [21] O. Ronneberger, P. Fischer, and T. Brox. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, 2015.
  • [22] O. Russakovsky et al. Imagenet large scale visual recognition challenge. International Journal of Computer Vision, 115(3):211–252, 2015.
  • [23] A. Sangole. Data-Driven Modeling Using Spherical Self-Organizing Maps. PhD thesis, Western University, 2002.
  • [24] S. Sarraf, G. Tofighi, et al. Deepad: Alzheimer’s disease classification via deep convolutional neural networks using mri and fmri. bioRxiv, page 070441, 2016.
  • [25] M. Selver et al.

    Semiautomatic transfer function initialization for abdominal visualization using self-generating hierarchical radial basis function.

    IEEE Transactions on Visualization and Computer Graphics, 15:395–409, May 2009.
  • [26] K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.
  • [27] C. Studholme, D. L. Hill, and D. J. Hawkes. An overlap invariant entropy measure of 3d medical image alignment. Pattern recognition, 32(1):71–86, 1999.
  • [28] C. H. Sudre et al. Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. In Deep Learning in Medical Image Analysis for Clinical Decision Support, pages 240–248. Springer, 2017.
  • [29] N. Tajbakhsh et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging, 35(5):1299–1312, 2016.
  • [30] K. C. Wong et al. 3d segmentation with exponential logarithmic loss for highly unbalanced object sizes. In International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, 2018.
  • [31] M. H. Yap et al. Automated breast ultrasound lesions detection using convolutional neural networks. IEEE journal of biomedical and health informatics, 22(4):1218–1226, 2018.
  • [32] J. Yosinski, J. Clune, Y. Bengio, and H. Lipson. How transferable are features in deep neural networks? In Advances in neural information processing systems, 2014.