Training deep convolutional neural networks with active learning for exudate classification in eye fundus images

Otálora, Sebastian (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; Perdomo, Oscar (Universidad Nacional de Colombia) ; González, Fabio (Universidad Nacional de Colombia) ; Müller, Henning (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis))

Training deep convolutional neural network for classification in medical tasks is often difficult due to the lack of annotated data sam-ples. Deep convolutional networks (CNN) has been successfully used as an automatic detection tool to support the grading of diabetic retinopa-thy and macular edema. Nevertheless, the manual annotation of exu-dates in eye fundus images used to classify the grade of the DR is very time consuming and repetitive for clinical personnel. Active learning al-gorithms seek to reduce the labeling effort in training machine learning models. This work presents a label-efficient CNN model using the ex-pected gradient length, an active learning algorithm to select the most informative patches and images, converging earlier and to a better local optimum than the usual SGD (Stochastic Gradient Descent) strategy. Our method also generates useful masks for prediction and segments regions of interest.


Faculty:
Economie et Services
School:
HEG VS HES-SO Valais-Wallis - Haute Ecole de Gestion & Tourisme
Subject(s):
Economie/gestion
Publisher:
Cham, Springer
Date:
Cham
Springer
2017
Pagination:
pp. 146-154
Published in
LABELS 2017, CVII 2017, STENT 2017 : intravascular imaging and computer assisted stenting, and large-scale annotation of biomedical data and expert label synthesis
DOI:
ISBN:
978-3-319-67533-6
Appears in Collection:

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 Record created 2017-11-02, last modified 2018-11-09

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