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Machine Learning Approaches for Medical Image Analysis

Call for Papers

Machine learning approaches are currently gaining a lot of attention for their wide applications. They have been increasingly successful in image-based diagnosis, disease prognosis, and risk assessment. The development of robust and reliable machine learning approaches for medical image application such as segmentation, abnormal detection, object detection, and computer aided diagnosis system is still highly challenging due to the high heterogeneity in the medical images.

The special issue aims to cover the applications of machine learning to medical image analysis in order to provide the reader with a dedicated discussion and cover the state of the art, open challenges, and overview of research directions and technologies that will become important in the future.

This special issue seeks high-quality research papers to cover the different research questions on machine learning applications in medical image analysis. We are targeting original research works in this field, covering new theories, new algorithms, and new implementations and applications for medical image analytics.

Potential topics include but are not limited to the following:

  • Recent advances of machine learning techniques in medical image analysis
  • Machine learning for cancer (breast, skin, prostate, etc.) detection/ diagnosis
  • Machine learning for medical image segmentation
  • Machine learning for high accuracy computer-aided detection/diagnosis systems
  • Machine learning for big data analysis of medical images and its applications
  • Learning from multiple modalities of imaging data
  • The use of machine learning for image classification, object detection, segmentation, registration, and other tasks
  • Performance evaluation of machine learning approaches in medical image analysis
  • Open challenges and directions for future research

Authors can submit their manuscripts through the Manuscript Tracking System at

Submission DeadlineFriday, 18 May 2018
Publication DateOctober 2018

Papers are published upon acceptance, regardless of the Special Issue publication date.

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Guest Editors