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EczemaNet: Automating Detection and Severity Assessment of Atopic Dermatitis

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Machine Learning in Medical Imaging (MLMI 2020)

Abstract

Atopic dermatitis (AD), also known as eczema, is one of the most common chronic skin diseases. AD severity is primarily evaluated based on visual inspections by clinicians, but is subjective and has large inter- and intra-observer variability in many clinical study settings. To aid the standardisation and automating the evaluation of AD severity, this paper introduces a CNN computer vision pipeline, EczemaNet, that first detects areas of AD from photographs and then makes probabilistic predictions on the severity of the disease. EczemaNet combines transfer and multitask learning, ordinal classification, and ensembling over crops to make its final predictions. We test EczemaNet using a set of images acquired in a published clinical trial, and demonstrate low RMSE with well-calibrated prediction intervals. We show the effectiveness of using CNNs for non-neoplastic dermatological diseases with a medium-size dataset, and their potential for more efficiently and objectively evaluating AD severity, which has greater clinical relevance than mere classification.

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Notes

  1. 1.

    As well as the area of the affected region in the case of EASI.

  2. 2.

    RoI, of arbitrary size, were labelled by 3 volunteers given a set of 50 expert-labelled images, where 1 volunteer was instructed directly by an expert. 431 photos were deemed difficult to label by the volunteers and hence left out of our dataset.

  3. 3.

    The full data pipeline is provided in Supp. Fig. 1.

  4. 4.

    In practice, EASI and SASSAD are assessed across different regions of the body, which we do not consider in this work.

  5. 5.

    Crops were preprocessed by bilinearly resampling to 224 \(\times \) 224px.

  6. 6.

    We convolve the probability mass functions of the predicted severity of the 7 disease signs, assuming that the predictions are independent random variables.

  7. 7.

    We also observed a similar ranking across models for SASSAD (Supp. Figure 3) and TISS (Supp. Figure 4), as well as across the individual signs.

  8. 8.

    The selection of base architecture was determined experimentally. MobileNet [13] provided greater benefits over other base architectures including VGG-16/19 [20], ResNet-50 [12], and Inception-v3 [22] (Supp. Fig. 5).

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Correspondence to Reiko J. Tanaka .

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Pan, K., Hurault, G., Arulkumaran, K., Williams, H.C., Tanaka, R.J. (2020). EczemaNet: Automating Detection and Severity Assessment of Atopic Dermatitis. In: Liu, M., Yan, P., Lian, C., Cao, X. (eds) Machine Learning in Medical Imaging. MLMI 2020. Lecture Notes in Computer Science(), vol 12436. Springer, Cham. https://doi.org/10.1007/978-3-030-59861-7_23

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  • DOI: https://doi.org/10.1007/978-3-030-59861-7_23

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