Abstract
Correctly classifying a skin lesion is one of the first steps towards treatment. We propose a novel convolutional neural network (CNN) architecture for skin lesion classification designed to learn based on information from multiple image resolutions while leveraging pretrained CNNs. While traditional CNNs are generally trained on a single resolution image, our CNN is composed of multiple tracts, where each tract analyzes the image at a different resolution simultaneously and learns interactions across multiple image resolutions using the same field-of-view. We convert a CNN, pretrained on a single resolution, to work for multi-resolution input. The entire network is fine-tuned in a fully learned end-to-end optimization with auxiliary loss functions. We show how our proposed novel multi-tract network yields higher classification accuracy, outperforming state-of-the-art multi-scale approaches when compared over a public skin lesion dataset.
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Acknowledgments
Thanks to the Natural Sciences and Engineering Research Council (NSERC) of Canada for funding and to the NVIDIA Corporation for the donation of a Titan X GPU used in this research.
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Kawahara, J., Hamarneh, G. (2016). Multi-resolution-Tract CNN with Hybrid Pretrained and Skin-Lesion Trained Layers. In: Wang, L., Adeli, E., Wang, Q., Shi, Y., Suk, HI. (eds) Machine Learning in Medical Imaging. MLMI 2016. Lecture Notes in Computer Science(), vol 10019. Springer, Cham. https://doi.org/10.1007/978-3-319-47157-0_20
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DOI: https://doi.org/10.1007/978-3-319-47157-0_20
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