Cost-Effective Active Learning for Melanoma Segmentation

Code

• github.com/imatge-upc/medical-2017-nipsw
  Officialnone★ 0
• github.com/marc-gorriz/CEAL-Medical-Image-Segmentation
  tf★ 0

Abstract

We propose a novel Active Learning framework capable to train effectively a convolutional neural network for semantic segmentation of medical imaging, with a limited amount of training labeled data. Our contribution is a practical Cost-Effective Active Learning approach using dropout at test time as Monte Carlo sampling to model the pixel-wise uncertainty and to analyze the image information to improve the training performance. The source code of this project is available at https://marc-gorriz.github.io/CEAL-Medical-Image-Segmentation/ .

Tasks

Reproductions