Knowledge Based Versus Data Based

A Historical Perspective on a Continuum of Methodologies for Medical Image Analysis
  • Peter Savadjiev
    Correspondence
    Corresponding author. Department of Diagnostic Radiology, McGill University, Room B02 9389, 1001 Decarie Boulevard, Montreal, Quebec H4A 3J1, Canada.
    Affiliations
    Department of Diagnostic Radiology, McGill University, Room B02 9389, 1001 Decarie Boulevard, Montreal, Quebec H4A 3J1, Canada

    School of Computer Science, McGill University, Montreal, Quebec, Canada

    Medical Physics Unit, Department of Oncology, McGill University, Montreal, Quebec, Canada

    Augmented Intelligence & Precision Health Laboratory (AIPHL), Department of Diagnostic Radiology, Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada
    Search for articles by this author
  • Caroline Reinhold
    Affiliations
    Department of Diagnostic Radiology, McGill University, Room B02 9389, 1001 Decarie Boulevard, Montreal, Quebec H4A 3J1, Canada

    Augmented Intelligence & Precision Health Laboratory (AIPHL), Department of Diagnostic Radiology, Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada
    Search for articles by this author
  • Diego Martin
    Affiliations
    Department of Diagnostic Radiology, McGill University, Room B02 9389, 1001 Decarie Boulevard, Montreal, Quebec H4A 3J1, Canada

    Augmented Intelligence & Precision Health Laboratory (AIPHL), Department of Diagnostic Radiology, Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada
    Search for articles by this author
  • Reza Forghani
    Affiliations
    Department of Diagnostic Radiology, McGill University, Room B02 9389, 1001 Decarie Boulevard, Montreal, Quebec H4A 3J1, Canada

    Augmented Intelligence & Precision Health Laboratory (AIPHL), Department of Diagnostic Radiology, Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada

    Segal Cancer Centre and Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada

    Gerald Bronfman Department of Oncology, McGill University, Montreal, Quebec, Canada

    Department of Otolaryngology–Head and Neck Surgery, McGill University, Montreal, Quebec, Canada
    Search for articles by this author
Published:September 17, 2020DOI:https://doi.org/10.1016/j.nic.2020.06.002

      Keywords

      To read this article in full you will need to make a payment
      Purchase one-time access
      Subscribers receive full online access to your subscription and archive of back issues up to and including 2002.
      Content published before 2002 is available via pay-per-view purchase only.
      Subscribe to Neuroimaging Clinics
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • LeCun Y.
        • Bengio Y.
        • Hinton G.
        Deep learning.
        Nature. 2015; 521: 436-444
        • Schier R.
        Artificial intelligence and the practice of radiology: an alternative view.
        J Am Coll Radiol. 2018; 15: 1004-1007
        • Tesler L.
        (Available at:) (Accessed March 23, 2020)
        • Yuille A.L.
        • Liu C.
        Deep nets: what have they ever done for vision?.
        (Available at:) (Accessed March 23, 2020)
        • Lipton Z.C.
        • Steinhardt J.
        Troubling trends in machine learning scholarship.
        (Available at:) (Accessed March 23, 2020)
        • Maier-Hein L.
        • Eisenmann M.
        • Reinke A.
        • et al.
        Why rankings of biomedical image analysis competitions should be interpreted with care.
        Nat Commun. 2018; 9: 5217
        • Fischer W.
        • Moudgalya S.S.
        • Cohn J.D.
        • et al.
        Sparse coding of pathology slides compared to transfer learning with deep neural networks.
        BMC Bioinformatics. 2018; 19: 489
        • Kraus W.L.
        Editorial: would you like a hypothesis with those data? Omics and the age of discovery science.
        Mol Endocrinol. 2015; 29: 1531-1534
        • Mazzocchi F.
        Could Big Data be the end of theory in science? A few remarks on the epistemology of data-driven science.
        EMBO Rep. 2015; 16: 1250-1255
        • Anand R.
        • Mehrotra K.G.
        • Mohan C.K.
        • et al.
        An improved algorithm for neural network classification of imbalanced training sets.
        IEEE Trans Neural Netw. 1993; 4: 962-969
        • Johnson J.M.
        • Khoshgoftaar T.M.
        Survey on deep learning with class imbalance.
        J Big Data. 2019; 6: 27
        • Buolamwini J.
        • Gebru T.
        Gender shades: intersectional accuracy disparities in commercial gender classification.
        Proc Mach Learn Res. 2018; 81: 1-15
        • Buolamwini J.
        Gender shades: intersectional phenotypic and demographic evaluation of face datasets and gender classifiers.
        MIT Master's Thesis, 2017
        • Caliskan A.
        • Bryson J.J.
        • Narayanan A.
        Semantics derived automatically from language corpora contain human-like biases.
        Science. 2017; 14: 183-186
        • Selvaraju R.R.
        • Cogswell M.
        • Das A.
        • et al.
        Grad-CAM: visual explanations from deep networks via gradient-based localization.
        Int J Comput Vis. 2020; 128: 336-359
        • George D.
        • Lehrach W.
        • Kansky K.
        • et al.
        A generative vision model that trains with high data efficiency and breaks text-based CAPTCHAs.
        Science. 2017; 358: eaag2612
        • Kortylewski A.
        • Liu Q.
        • Wang H.
        • et al.
        Combining compositional models and deep networks for robust object classification under occlusion.
        (Available at:) (Accessed March 23, 2020)
        • Kinney E.L.
        Medical expert systems. Who needs them?.
        Chest. 1987; 91: 3-4
        • Shortliffe E.H.
        Medical expert systems – knowledge tools for physicians.
        West J Med. 1986; 145: 830-839
        • Stansfield S.A.
        ANGY: a rule-based expert system for automatic segmentation of coronary vessels from digital subtracted angiograms.
        IEEE Trans Pattern Anal Mach Intell. 1986; 2: 188-199
        • Hubel D.H.
        • Wiesel T.N.
        Receptive fields of single neurones in the cat's striate cortex.
        J Physiol. 1959; 124: 574-591
        • Hubel D.H.
        • Wiesel T.N.
        Receptive fields, binocular interaction and functional architecture in the cat's visual cortex.
        J Physiol. 1962; 160: 106-154
        • Marr D.
        Vision: a computational investigation into the human representation and processing of visual information.
        W. H. Freeman and Company, New York1982
        • McInerney T.
        • Terzopoulos D.
        Deformable models in medical image analysis: a survey.
        Med Image Anal. 1996; 1: 91-108
        • Kass M.
        • Witkin A.
        • Terzopoulos D.
        Snakes: active contour models.
        Int J Comput Vis. 1988; 1: 321-331
        • Osher S.
        • Sethian J.A.
        Fronts propagating with curvature-dependent speed: algorithms based on Hamilton--Jacobi formulations.
        J Comput Phys. 1988; 79: 12-49
        • Lorigo L.M.
        • Faugeras O.D.
        • Grimson W.E.
        • et al.
        CURVES: curve evolution for vessel segmentation.
        Med Image Anal. 2001; 5: 195-206
        • Paragios N.
        A level set approach for shape-driven segmentation and tracking of the left ventricle.
        IEEE Trans Med Imaging. 2003; 22: 773-776
        • Vasilevskiy A.
        • Siddiqi K.
        Flux maximizing geometric flows.
        IEEE Trans Pattern Anal Mach Intell. 2002; 24: 1565-1578
        • Malladi R.
        • Sethian J.A.
        • Vemuri B.C.
        Shape modeling with front propagation: a level set approach.
        IEEE Trans Pattern Anal Mach Intell. 1995; 17: 158-175
        • Chan T.
        • Vese L.
        Active contours without edges.
        IEEE Trans Image Process. 2001; 10: 266-277
        • Paragios N.
        • Deriche R.
        Geodesic active regions and level set methods for supervised texture segmentation.
        Int J Comput Vis. 2002; 46: 223-247
        • Tikhonov A.N.
        • Arsenin V.Y.
        Solution of ill-posed problems.
        Winston & Sons, Washington, DC1977
        • Parent P.
        • Zucker S.W.
        Trace inference, curvature consistency, and curve detection.
        IEEE Trans Pattern Anal Mach Intell. 1989; 11: 823-839
        • Savadjiev P.
        • Campbell J.S.W.
        • Pike G.B.
        • et al.
        3D curve inference for diffusion MRI regularization and fibre tractography.
        Med Image Anal. 2006; 10: 799-813
        • Savadjiev P.
        • Strijkers G.J.
        • Bakermans A.J.
        • et al.
        Heart wall myofibers are arranged in minimal surfaces to optimize organ function.
        Proc Natl Acad Sci U S A. 2012; 109: 9248-9253
        • Pizer S.M.
        • Fletcher P.T.
        • Joshi S.
        • et al.
        Deformable M-Reps for 3D medical image segmentation.
        Int J Comput Vis. 2003; 55: 85-106
        • Yushkevich P.A.
        • Zhang H.
        • Gee J.C.
        Continuous medial representation for anatomical structures.
        IEEE Trans Med Imaging. 2006; 25: 1547-1564
        • Bajcsy R.
        • Kovacic S.
        Multiresolution elastic matching.
        Comput Vision, Graphics, Image Process. 1989; 46: 1-21
        • Gee J.C.
        • Reivich M.
        • Bajcsy R.
        Elastically deforming atlas to match anatomical brain images.
        J Comput Assist Tomogr. 1993; 17: 225-236
        • Christensen G.E.
        • Rabbitt R.D.
        • Miller M.I.
        Deformable templates using large deformation kinematics.
        IEEE Trans Image Process. 1996; 5: 1435-1447
        • Thompson P.
        • Toga A.W.
        A surface-based technique for warping three-dimensional images of the brain.
        IEEE Trans Med Imaging. 1996; 15: 402-417
        • Avants B.
        • Gee J.C.
        Geodesic estimation for large deformation anatomical shape averaging and interpolation.
        Neuroimage. 2004; 23: S139-S150
        • Joshi S.
        • Davis B.
        • Jomier M.
        • et al.
        Unbiased diffeomorphic atlas construction for computational anatomy.
        Neuroimage. 2004; 23: S151-S160
        • Van Leemput K.
        • Maes F.
        • Vandermeulen D.
        • et al.
        Automated segmentation of multiple sclerosis lesions by model outlier detection.
        IEEE Trans Med Imaging. 2001; 20: 677-688
        • Lustberg T.
        • van Soest J.
        • Gooding M.
        • et al.
        Clinical evaluation of atlas and deep learning based automatic contouring for lung cancer.
        Radiother Oncol. 2018; 126: 312-317
        • Commonwick O.
        • Grégoire V.
        • Malandain G.
        Atlas-based delineation of lymph node levels in head and neck computed tomography images.
        Radiother Oncol. 2008; 87: 281-289
        • Fritscher K.D.
        • Peroni M.
        • Zaffino P.
        • et al.
        Automatic segmentation of head and neck CT images for radiotherapy treatment planning using multiple atlases, statistical appearance models, and geodesic active contours.
        Med Phys. 2014; 41: 051910
        • Lee H.
        • Lee E.
        • Kim N.
        • et al.
        Clinical evaluation of commercial atlas-based auto-segmentation in the head and neck region.
        Front Oncol. 2019; 9: 239
        • Stapleford L.J.
        • Lawson J.D.
        • Perkins C.
        • et al.
        Evaluation of automatic atlas-based lymph node segmentation for head-and-neck cancer.
        Int J Radiat Oncol Biol Phys. 2010; 77: 959-966
        • Gilbert K.
        • Bai W.
        • Mauger C.
        • et al.
        Independent left ventricular morphometric atlases show consistent relationships with cardiovascular risk factors: a UK biobank study.
        Sci Rep. 2019; 9: 1130
        • Aljabar P.
        • Wolz R.
        • Srinivasan L.
        • et al.
        Combining morphological information in a manifold learning framework: application to neonatal MRI.
        Med Image Comput Comput Assist Interv. 2010; 13: 1-8
        • Gerber S.
        • Tasdizen T.
        • Joshi S.
        • et al.
        On the manifold structure of the space of brain images.
        Med Image Comput Comput Assist Interv. 2009; 12: 305-312
        • Pless R.
        • Souvenir R.
        A survey of manifold learning for images.
        Information Processing Society of Japan Transactions on Computer Vision and Applications. 2009; 1: 83-94
        • Okada T.
        • Linguraru M.G.
        • Hori M.
        • et al.
        Abdominal multi-organ segmentation from CT images using conditional shape-location and unsupervised intensity priors.
        Med Image Anal. 2015; 26: 1-18
        • Barlow H.B.
        Single units and sensation: a neuron doctrine for perceptual psychology?.
        Perception. 1972; : 371-394
        • Candès E.J.
        • Romberg J.K.
        • Tao T.
        Stable signal recovery from incomplete and inaccurate measurements.
        Commun Pure Appl Math. 2006; 59: 1207-1223
        • Donoho D.L.
        Compressed sensing.
        IEEE Trans Inf Theory. 2006; 52: 1289-1306
        • Bilgic B.
        • Setsompop K.
        • Cohen-Adad J.
        • et al.
        Accelerated diffusion spectrum imaging with compressed sensing using adaptive dictionaries.
        Magn Reson Med. 2012; 68: 1747-1754
        • Cauley S.F.
        • Xi Y.
        • Bilgic B.
        • et al.
        Fast reconstruction for multichannel compressed sensing using a hierarchically semiseparable solver.
        Magn Reson Med. 2015; 73: 1034-1040
        • Lustig M.
        • Donoho D.
        • Pauly J.M.
        Sparse MRI: the application of compressed sensing for rapid MR imaging.
        Magn Reson Med. 2007; 58: 1182-1195
        • Mairal J.
        • Elad M.
        • Sapiro G.
        Sparse representation for color image restoration.
        IEEE Trans Image Process. 2008; 17: 53-69
        • Papyan V.
        • Romano Y.
        • Elad M.
        Convolutional neural networks analyzed via convolutional sparse coding.
        J Mach Learn Res. 2017; 18: 1-52
        • Duncan J.
        • Ayache N.
        Medical image analysis: progress over two decades and the challenges ahead. pattern analysis and machine intelligence.
        IEEE Transactions on. 2000; 22: 85-106
        • Iglesias J.E.
        • Sabuncu M.R.
        Multi-atlas segmentation of biomedical images: a survey.
        Med Image Anal. 2015; 24: 205-219
        • Zhou S.K.
        Medical image recognition, segmentation and parsing: machine learning and multiple object approaches.
        Academic Press, San Francisco (CA)2015
        • Zhou S.K.
        • Rueckert D.
        • Fichtinger G.
        Handbook of medical image computing and computer assisted intervention.
        Academic Press, San Francisco (CA)2020