XRay-survey.md

• Self-Transfer Learning for Fully Weakly Supervised Object Localization
  • by Sangheum Hwang and Hyo-Eun Kim (both from Lunit)
  • Arxiv:1602, MICCAI 2016
  • Datasets used: KIT(training), Shenzhen(test), MC set(test)
    • Localization annotation is obtained by human clinicians
• LEARNING TO DIAGNOSE FROM SCRATCH BY EXPLOITING DEPENDENCIES AMONG LABELS [paper]
  • Li Yao, Eric Poblenz, Dmitry Dagunts, Ben Covington, Devon Bernard, Kevin Lyman
  • Arxiv:1710
• Learning to detect chest radiographs containing lung nodules using visual attention networks [paper]
  • Emanuele Pesce, Petros-Pavlos Ypsilantis, Samuel Withey, Robert Bakewell, Vicky Goh, Giovanni Montana
  • Arxiv:1712
• Abnormality Detection and Localization in Chest X-Rays using Deep Convolutional Neural Networks [paper]
  • Mohammad Tariqul Islam, Md Abdul Aowal, Ahmed Tahseen Minhaz, Khalid Ashraf
  • Arxiv:1705
• Deep Learning with Lung Segmentation and Bone Shadow Exclusion Techniques for Chest X-Ray Analysis of Lung Cancer [paper]

Survey papers

• A Survey on Deep Learning in Medical Image Analysis [paper]
  • Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen A.W.M. van der Laak, Bram van Ginneken, Clara I. Sanchez
  • Arxiv:1702
• Deep Learning Applications in Medical Image Analysis [paper]
  • Justin Ker, Lipo Wang, Jai Rao, and Tchoyoson Lim