NIMG-31. CLINICALLY RELIABLE DEEP LEARNING MODEL FOR DIFFERENTIATION OF GLIOBLASTOMA FROM SINGLE BRAIN METASTASIS: ESTIMATING UNCERTAINTY WITH DEEP ENSEMBLES

Melanoma detection with a deep learning model

Background: Skin cancer is one of the most common forms of cancer in the world and melanoma is the deadliest type of skin cancer. Both melanoma and melanocytic nevi begin in melanocytes (cells that produce melanin). However, melanocytic nevi are benign whereas melanoma is malignant. This work proposes a deep learning model for classification of these two lesions.    Methods: In this analytic s...

Bayesian Deep Learning | Uncertainty in Deep Learning

A Deep Model for Super-resolution Enhancement from a Single Image

This study presents a method to reconstruct a high-resolution image using a deep convolution neural network. We propose a deep model, entitled Deep Block Super Resolution (DBSR), by fusing the output features of a deep convolutional network and a shallow convolutional network. In this way, our model benefits from high frequency and low frequency features extracted from deep and shallow networks...

Deep Insights | Uncertainty in Deep Learning

Until now we have mostly studied the proposed approximate inference techniques empirically. In this chapter we turn to a more theoretical analysis, and concentrate mostly on the case of dropout, as it seems to be the most widely used among the various stochastic regularisation techniques. We begin by suggesting practical considerations for getting good uncertainty estimates, followed by a revie...

Uncertainty in Deep Learning

Deep learning has attracted tremendous attention from researchers in various fields of information engineering such as AI, computer vision, and language processing [Kalchbrenner and Blunsom, 2013; Krizhevsky et al., 2012; Mnih et al., 2013], but also from more traditional sciences such as physics, biology, and manufacturing [Anjos et al., 2015; Baldi et al., 2014; Bergmann et al., 2014]. Neural...