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Fast convolutional neural network training using selective data sampling: Application to hemorrhage

摘要：Abstract—Convolutional neural networks (CNNs) are deep
learning network architectures that have pushed forward the
state-of-the-art in a range of computer vision applications and
are increasingly popular in medical image analysis. However,
training of CNNs is time-consuming and challenging. In medical
image analysis tasks, the majority of training examples are easy
to classify and therefore contribute little to the CNN learning
process. In this paper, we propose a method to improve and
speed-up the CNN training for medical image analysis tasks
by dynamically selecting misclassified negative samples during
training. Training samples are heuristically sampled based on
classification by the current status of the CNN. Weights are
assigned to the training samples and informative samples are
more likely to be included in the next CNN training iteration.
We evaluated and compared our proposed method by training
a CNN with (SeS) and without (NSeS) the selective sampling
method. We focus on the detection of hemorrhages in color
fundus images. A decreased training time from 170 epochs to
60 epochs with an increased performance – on par with two
human experts – was achieved with areas under the receiver
operating characteristics curve of 0.894 and 0.972 on two data
sets. The SeS CNN statistically outperformed the NSeS CNN on
an independent test set.
Index Terms—Convolutional neural network, deep learning,
hemorrhage, selective sampling

本文标签： Fast convolutional neural network training using selective data sampling Application to hemorrhage

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