Deep neural network ensemble by data augmentation and bagging for skin lesion classification

2018-07-15 · Manik Goyal, Jagath C. Rajapakse

This work summarizes our submission for the Task 3: Disease Classification of ISIC 2018 challenge in Skin Lesion Analysis Towards Melanoma Detection. We use a novel deep neural network (DNN) ensemble architecture introduced by us that can effectively classify skin lesions by using data-augmentation and bagging to address paucity of data and prevent over-fitting. The ensemble is composed of two DNN architectures: Inception-v4 and Inception-Resnet-v2. The DNN architectures are combined in to an ensemble by using a $1\times1$ convolution for fusion in a meta-learning layer.

📄 PDF Abstract BibTeX arXiv:1807.05496

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Tasks

Data Augmentation General ClassificationLesion ClassificationMeta-Learning Skin Lesion Classification

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Inception-ResNet-v2 Reduction-B Inception-ResNet-v2 Reduction-B is an image model block used in the Inception-ResNet-v2 architecture.

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Inception-ResNet-v2-B Inception-ResNet-v2-B is an image model block for a 17 x 17 grid used in the Inception-ResNet-v2 architecture. It…

Inception-ResNet-v2-C Inception-ResNet-v2-C is an image model block for an 8 x 8 grid used in the Inception-ResNet-v2 architecture. It…

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Average Pooling 설명 없음

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In this report, we introduce the outline of our system in Task 3: Disease Classification of ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection. We fine-tuned multiple pre-trained neural network models based on Sq…

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