Oct 7, 2019 � In this paper, we highlight the limitations of a variance based metric, and propose a novel uncertainty metric based on the overlap of the output distributions.
Quantifying uncertainty of deep neural networks in skin lesion classification. Pieter Van Molle, Tim Verbelen, Cedric De Boom, Bert Vankeirsbilck,. Jonas De�...
This paper highlights the limitations of a variance based metric, and proposes a novel uncertainty metric based on the overlap of the output distributions,�...
In this paper, we highlight the limitations of a variance based metric, and propose a novel uncertainty metric based on the overlap of the output distributions.
Oct 5, 2022 � Quantifying uncertainty of deep neural networks in skin lesion classification. Uncertainty for Safe Utilization of. Machine Learning in�...
We proposed an uncertainty quantification-based model for skin cancer classification. We applied three UQ methods: MC dropout, Ensemble MC dropout and DE.
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In this work, we explore the use of uncertainty estimation techniques and metrics for deep neural networks based on Monte-Carlo sampling.
Quantifying Uncertainty of Deep Neural Networks in Skin Lesion Classification � Pieter Van Molle � Tim Verbelen � Cedric De Boom � Bert Vankeirsbilck � Jonas De�...
Oct 31, 2022 � This article presents a framework for the segmentation and multiclass classification of skin lesion images by incorporating uncertainty�...
Jan 7, 2022 � A web server that performs an intuitive in-depth analysis of uncertainty in commonly used skin cancer classification models based on convolutional neural�...