Skin lesion classification using ensembles of multi-resolution EfficientNets with meta data

In this paper, we describe our method for the ISIC 2019 Skin Lesion Classification Challenge. The challenge comes with two tasks. For task 1, skin lesions have to be classified based on dermoscopic images. For task 2, dermoscopic images and additional patient meta data are used. Our deep learning-based method achieved first place for both tasks. The are several problems we address with our method. First, there is an unknown class in the test set which we cover with a data-driven approach. Second, there is a severe class imbalance that we address with loss balancing. Third, there are images with different resolutions which motivates two different cropping strategies and multi-crop evaluation. Last, there is patient meta data available which we incorporate with a dense neural network branch. • We address skin lesion classification with an ensemble of deep learning models including EfficientNets, SENet, and ResNeXt WSL, selected by a search strategy. • We rely on multiple model input resolutions and employ two cropping strategies for training. We counter severe class imbalance with a loss balancing approach. • We predict an additional, unknown class with a data-driven approach and we make use of patient meta data with an additional input branch.

Subjects

Convolutional neural network

Convolutional neural networks

Deep Learning

Multi-class skin lesion classification

DDC Class

510: Mathematik

Funding(s)

Publikationsfonds 2020

More Funding Information

Supported by the Forschungszentrum Medizintechnik Hamburg (02fmthh2017).

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https://creativecommons.org/licenses/by/4.0/

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1-s2.0-S2215016120300832-main.pdf

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Skin lesion classification using ensembles of multi-resolution EfficientNets with meta data