论文信息 - Monte-Carlo Sampling applied to Multiple Instance Learning for Whole Slide Image Classification

Monte-Carlo Sampling applied to Multiple Instance Learning for Whole Slide Image Classification

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 performance on the differentiation between sun exposed and not sun exposed pieces of skin tissue.

Verónica Vilaplana | Marc Combalia | Verónica Vilaplana | M. Combalia | V. Vilaplana

[1] Ellen T. Gelfand,et al. A Novel Approach to High-Quality Postmortem Tissue Procurement: The GTEx Project , 2015, Biopreservation and biobanking.

[2] Wenyu Liu,et al. Revisiting multiple instance neural networks , 2016, Pattern Recognit..

[3] Joel H. Saltz,et al. Patch-Based Convolutional Neural Network for Whole Slide Tissue Image Classification , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[4] Max Welling,et al. Deep Learning with Permutation-invariant Operator for Multi-instance Histopathology Classification , 2017, ArXiv.

[5] Linda G. Shapiro,et al. Multi-instance multi-label learning for whole slide breast histopathology , 2016, SPIE Medical Imaging.

[6] Max Welling,et al. Attention-based Deep Multiple Instance Learning , 2018, ICML.