Self-Transfer Learning for Weakly Supervised Lesion Localization

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• 关键词：Weakly supervised learning ; Lesion localization ; Convolutional neural networks
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9901
• 期：1
• 页码：239-246
• 全文大小：3,477 KB
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• 作者单位：Sangheum Hwang (18)
  Hyo-Eun Kim (18)
  
  18. Lunit Inc., Seoul, Korea
  
• 丛书名：Medical Image Computing and Computer-Assisted Intervention ¨C MICCAI 2016
• ISBN：978-3-319-46723-8
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9901

文摘

Recent advances of deep learning have achieved remarkable performances in various computer vision tasks including weakly supervised object localization. Weakly supervised object localization is practically useful since it does not require fine-grained annotations. Current approaches overcome the difficulties of weak supervision via transfer learning from pre-trained models on large-scale general images such as ImageNet. However, they cannot be utilized for medical image domain in which do not exist such priors. In this work, we present a novel weakly supervised learning framework for lesion localization named as self-transfer learning (STL). STL jointly optimizes both classification and localization networks to help the localization network focus on correct lesions without any types of priors. We evaluate STL framework over chest X-rays and mammograms, and achieve significantly better localization performance compared to previous weakly supervised localization approaches.