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Skin Lesion Classification Using Deep Multi-scale Convolutional Neural Networks

2017-03-04 · Terrance DeVries, Dhanesh Ramachandram

We present a deep learning approach to the ISIC 2017 Skin Lesion Classification Challenge using a multi-scale convolutional neural network. Our approach utilizes an Inception-v3 network pre-trained on the ImageNet dataset, which is fine-tuned for skin lesion classification using two different scales of input images.

📄 PDF Abstract BibTeX arXiv:1703.01402

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ClassificationGeneral ClassificationLesion ClassificationSkin Lesion Classification

Methods 이 논문이 사용한 방법론

Average Pooling 설명 없음
Auxiliary Classifier Auxiliary Classifiers are type of architectural component that seek to improve the convergence of very deep networks. They are classifier heads we attach to layers before the…
1x1 Convolution A 1 x 1 Convolution is a convolution with some special properties in that it can be used for dimensionality reduction,…
RMSProp RMSProp is an unpublished adaptive learning rate optimizer proposed by Geoff Hinton. The motivation…
Inception-v3 Module Inception-v3 Module is an image block used in the Inception-v3 architecture. This architecture is used on the coarsest (8 ×…
Max Pooling Max Pooling is a pooling operation that calculates the maximum value for patches of a feature map, and uses it to create a downsampled (pooled) feature map. It is usually…
Softmax The Softmax output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$…
Convolution A convolution is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the…

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