Now showing items 1-5 of 5

  • BCN20000: dermoscopic lesions in the wild 

    Combalia, Marc; Codella, Noel C. F.; Rotemberg, Veronica; Vilaplana Besler, Verónica (2019)
    External research report
    Open Access
    This article summarizes the BCN20000 dataset, composed of 19424 dermoscopic images of skin lesions captured from 2010 to 2016 in the facilities of the Hospital Clínic in Barcelona. With this dataset, we aim to study the ...
  • Cascaded V-Net using ROI masks for brain tumor segmentation 

    Casamitjana Díaz, Adrià; Catà, Marcel; Sanchez Muriana, Irina; Combalia, Marc; Vilaplana Besler, Verónica (Springer, 2018)
    Part of book or chapter of book
    Restricted access - publisher's policy
    This book constitutes revised selected papers from the Third International MICCAI Brainlesion Workshop, BrainLes 2017, as well as the International Multimodal Brain Tumor Segmentation, BraTS, and White Matter Hyperintensities, ...
  • Masked V-Net: an approach to brain tumor segmentation 

    Catà, Marcel; Casamitjana Díaz, Adrià; Sanchez Muriana, Irina; Combalia, Marc; Vilaplana Besler, Verónica (2017)
    Conference lecture
    Restricted access - publisher's policy
    This paper introduces Masked V-Net architecture, a variant of the recently introduced V-Net[13] that reformulates the residual connections and uses a ROI mask to constrain the network to train only on relevant voxels. ...
  • Monte-Carlo sampling applied to multiple instance learning for histological image classification 

    Combalia, Marc; Vilaplana Besler, Verónica (Springer, 2018)
    Conference lecture
    Restricted access - publisher's policy
    We propose a patch sampling strategy based on a sequential Monte-Carlo method for high resolution image classification in the context of Multiple Instance Learning. When compared with grid sampling and uniform sampling ...
  • Monte-Carlo sampling applied to multiple instance learning for whole slide image classification 

    Combalia, Marc; Vilaplana Besler, Verónica (2018)
    Conference lecture
    Open Access
    In this paper we propose a patch sampling strategy based on sequential Monte-Carlo methods for Whole Slide Image classification in the context of Multiple Instance Learning and show its capability to achieve high generalization ...