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Visualizing Uncertainty and Saliency Maps of Deep Convolutional Neural
  Networks for Medical Imaging Applications

Visualizing Uncertainty and Saliency Maps of Deep Convolutional Neural Networks for Medical Imaging Applications

5 July 2019
J. D. Seo
    MedIm
    FAtt
ArXivPDFHTML

Papers citing "Visualizing Uncertainty and Saliency Maps of Deep Convolutional Neural Networks for Medical Imaging Applications"

2 / 2 papers shown
Title
AP-MTL: Attention Pruned Multi-task Learning Model for Real-time
  Instrument Detection and Segmentation in Robot-assisted Surgery
AP-MTL: Attention Pruned Multi-task Learning Model for Real-time Instrument Detection and Segmentation in Robot-assisted Surgery
Mobarakol Islam
V. Vibashan
Hongliang Ren
32
28
0
10 Mar 2020
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
289
9,167
0
06 Jun 2015
1