The objective of this work is to estimate domain shifts in dermoscopic datasets from International Skin Imaging Collaboration (ISIC) archive. We analyzed the datasets from different clinics that comprise of various image acquisition systems and lighting conditions. Also, these images are obtained from a wide range of patients with various biological factors like skin color, age and gender. Based on the domain shifts calculated, datasets are split into in and out of domain regimes. Our focus is to quantify domain shifts in dermoscopic datasets and in-turn evaluate their influence on the performance of unsupervised domain adaptation methods. I will present the comparative analysis on how the domain adaptation methods are performing on the domain shifted datasets.


    Access

    Download


    Export, share and cite



    Title :

    Domain Shifts in Dermoscopic Datasets


    Contributors:

    Conference:


    Publication date :

    2022-11-08


    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    Sampling Methods to Balance Classes in Dermoscopic Skin Lesion Images

    Nguyen, Quynh T. / Jancic-Turner, Tanja / Kaur, Avneet et al. | Springer Verlag | 2024


    Parameterization of Dermoscopic Findings for the Internet-based Melanoma Screening System

    Iyatomi, Hitoshi / Oka, Hiroshi / Celebi, M. Emre et al. | IEEE | 2007



    Large-scale continuous datasets and their contribution to understanding activity shifts and distributional equity: the Melbourne experience

    Wigan, M. / Morris, J. / Association for European Transport | British Library Conference Proceedings | 2003


    Analysis of Classifier Training on Synthetic Data for Cross-Domain Datasets

    Cortes, Andoni / Rodriguez, Clemente / Velez, Gorka et al. | IEEE | 2022