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Classification of boar sperm head imagesusing Learning Vector Quantization

ESANN 2006, Classification of boar sperm head images using LVQ ... microscopic images of boar sperm heads (Leon/Spain) e.g. quality inspection after ...

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Classification of boar sperm head imagesusing Learning Vector Quantization

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    Classification of boar sperm head images using Learning Vector Quantization Rijksuniversiteit Groningen/ NL Mathematics and Computing Science http://www.cs.rug.nl/~biehl m.biehl@rug.nl Michael Biehl, Piter Pasma, Marten Pijl, Nicolai Petkov Lidia Snchez University of Len / Spain Electrical and Electronical Engineering semen fertility assessment: important problem in human / veterinary medicine medical diagnosis: - sophisticated techniques, e.g. staining methods - high accurracy determination of fertility evaluation of sample quality for animal breeding purposes - fast and cheap method of inspection Motivation here: - microscopic images of boar sperm heads (Leon/Spain) e.g. quality inspection after freezing and storage - distance-based classification, parameterized by prototypes - Learning Vector Quantization + Relevance Learning microscopic images of boar sperms normal (650) non-normal (710) example images, classified by experts (visual inspection) application of Learning Vector Quantization: - prototypes determined from example data - parameterize a distance based classification - plausible, straightforward to interpret/discuss with experts - include adaptive metrics in relevance learning aim: generalization ability classification of novel data after learning from examples Learning Vector Quantization (LVQ) ? Euclidean distance between data ? prototype w: LVQ1 given ?, update only the winner: Learning algorithms (sign acc. to class membership) prototype initialization: class-conditional means + random displacement (~70% correct classification) cross-validation scheme evaluation of performance - with respect to the training data, e.g. 90% of all data - with respect to test data 10% of all data average outcome over 10 realizations comparison of different LVQ systems (# of prototypes) ten-fold cross-validation: Generalized Learning Vector Quantization (GLVQ) given a single example, update the two winning prototypes : wJ from the same class as the example (correct winner) wK from the other class (wrong winner) [A.S. Sato and K. Yamada, NIPS 7, 1995)] Generalized Relevance LVQ (GRLVQ) GLVQ with modified distance measure vector of relevances, normalization GRLVQ - determines favorable positions of the prototypes - adapts the corresponding distance measure [B. Hammer, T. Villmann, Neural Networks 15: 1059-1068] Comparison of performance: estimated test error alg. 3/3 1/7 normal/non-normal prototypes - weak dependence on the number of prototypes inferior performance of GLVQ (cost function ? classification error) - recovered when including relevances mean (stand. dev.) only very few pixels are sufficient for successful classification test error: (all) 82.75%, (69) 82.75%, (15) 81.87% GRLVQ: resulting relevances Summary LVQ provides a transparent, plausible classification of microscopic boar sperm head images Performance: LVQ1 ? GLVQ ? GRLVQ satisfactory classification error (ultimate goal: estimation of sample composition) Relevances: very few relevant pixels, robust performance noisy labels / insufficient resolution?

    13. Histogram plaatje van relevance vectorHistogram plaatje van relevance vector

    LVQ1 demo
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