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Melanoma Detection by Means of Multiple Instance Learning

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Abstract

We present an application to melanoma detection of a multiple instance learning (MIL) approach, whose objective, in the binary case, is to discriminate between positive and negative sets of items. In the MIL terminology these sets are called bags and the items inside the bags are called instances. Under the hypothesis that a bag is positive if at least one of its instances is positive and it is negative if all its instances are negative, the MIL paradigm fits very well with images classification, since an image (bag) is in general classified on the basis of some its subregions (instances). In this work we have applied a MIL algorithm on some clinical data constituted by color dermoscopic images, with the aim to discriminate between melanomas (positive images) and common nevi (negative images). In comparison with standard classification approaches, such as the well known support vector machine, our method performs very well in terms both of accuracy and sensitivity. In particular, using a leave-one-out validation on a data set constituted by 80 melanomas and 80 common nevi, we have obtained the following results: accuracy = 92.50%, sensitivity = 97.50% and specificity = 87.50%. Since the results appear promising, we conclude that a MIL technique could be at the basis of more sophisticated tools useful to physicians in melanoma detection.

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Authors and Affiliations

  1. ICAR - National Research Council, Rende, Italy

    Annabella Astorino

  2. Department of Mathematics and Computer Science, University of Calabria, Rende, Italy

    Antonio Fuduli

  3. Bioinformatics Laboratory, Surgical and Medical Science Department - DSMC, University Magna Graecia, Catanzaro, Italy

    Pierangelo Veltri

  4. Department of Computer Engineering, Modeling, Electronics and Systems - DIMES, University of Calabria, Rende, Italy

    Eugenio Vocaturo

Authors
  1. Annabella Astorino
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  2. Antonio Fuduli
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  3. Pierangelo Veltri
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  4. Eugenio Vocaturo
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Corresponding author

Correspondence to Antonio Fuduli.

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Astorino, A., Fuduli, A., Veltri, P. et al. Melanoma Detection by Means of Multiple Instance Learning. Interdiscip Sci Comput Life Sci 12, 24–31 (2020). https://doi.org/10.1007/s12539-019-00341-y

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  • DOI: https://doi.org/10.1007/s12539-019-00341-y

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