Attention UW-Net: A fully connected model for automatic segmentation and annotation of chest X-ray.

Journal: Computers in biology and medicine
Published Date:

Abstract

BACKGROUND AND OBJECTIVE: Automatic segmentation and annotation of medical image plays a critical role in scientific research and the medical care community. Automatic segmentation and annotation not only increase the efficiency of clinical workflow, but also prevent overburdening of radiologists. The objective of this work is to improve the accuracy and give a probabilistic map for automatic annotation from small data set to reduce the use of tedious and prone to error manual annotations from chest X-rays.

Authors

  • Debojyoti Pal
    Artificial Intelligence and Data Science, Jio Institute, Navi Mumbai, 410206, India. Electronic address: Debojyoti.Pal@jioinstitute.edu.in.
  • Pailla Balakrishna Reddy
    Reliance Jio - Artificial Intelligence Centre of Excellence (AICoE), Hyderabad, India. Electronic address: balakrishna.pailla@ril.com.
  • Sudipta Roy
    CSE Department, Assam University Silchar, Assam, India.