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Dermoscopic Image Analysis for ISIC Challenge 2018

2018-07-24 · Jinyi Zou, Xiao Ma, Cheng Zhong, Yao Zhang

This short paper reports the algorithms we used and the evaluation performances for ISIC Challenge 2018. Our team participates in all the tasks in this challenge. In lesion segmentation task, the pyramid scene parsing network (PSPNet) is modified to segment the lesions. In lesion attribute detection task, the modified PSPNet is also adopted in a multi-label way. In disease classification task, the DenseNet-169 is adopted for multi-class classification.

📄 PDF Abstract BibTeX arXiv:1807.08948

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Tasks

AttributeClassificationGeneral ClassificationLesion SegmentationMulti-class ClassificationScene Parsing

Methods 이 논문이 사용한 방법론

ReLU How Do I Communicate to Expedia? How Do I Communicate to Expedia? – Call ☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056 for Live Support & Special Travel…
Batch Normalization 설명 없음
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…
Average Pooling 설명 없음
Pyramid Pooling Module A Pyramid Pooling Module is a module for semantic segmentation which acts as an effective global contextual prior. The motivation is that the problem of using a convolutional…
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…
Dilated Convolution 설명 없음
PSPNet PSPNet, or Pyramid Scene Parsing Network, is a semantic segmentation model that utilises a pyramid parsing module that exploits global context information by…

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