Pattern recognition (PR) plays a key role for developing quantitative monitoring from biomedical image data. Its goal is to extract meaningful components from images in order to determine hereafter higher level information. This can in turn be utilized for better decision making in a wide range of biomedical applications where quantification is crucial, e. g.​ for correct and early computer-aided diagnosis or phenotyping. In this field, various techniques are developed, such as segmentation, registration, fusion, classification, etc​. The most modern approaches dealing with PR employ deep learning, i. e.​ the development of neural networking architectures and adapted tensor-based operators for learning and predicting target knowledge from image samples.
The goal of the PR4BioImaging2021 special track (Pattern Recognition for Biomedical Quantitative Imaging in Computer-Aided Pathology 2021) is to promote the most recent and pertinent advances in this field by inviting highly reputed researchers for keynotes, and selecting articles by the reviewing process of CBMS 2021 (presented as orals or posters).


The topics include, but are not limited to:

  • Biomedical image analysis and processing: Image enhancement, segmentation, registration, classification, etc.
  • Machine and deep learning for biomedical image data.
  • Quantitative biomedical imaging: Computer Tomography (CT), Magnetic Resonance Imaging (MRI), Ultrasound, etc​.
  • Medical application should focus mainly on computer-aided diagnosis or computer-aided phenotyping.

Paper submission

  • Prospective authors are invited to submit papers in any of the topics listed above.
  • Instructions for preparing the manuscript (in Word and Latex formats) are available at: Call for Papers (main track)
  • Please also check the Guidelines.
  • Papers must be submitted electronically via the web-based submission system.


  • Antoine Vacavant, Université Clermont Auvergne, France
  • Robin Strand, Uppsala University, Sweden
  • Punam K Saha, University of Iowa, USA
  • Yan Xu, Beihang University, China
  • KC Santosh, University of South Dakota, USA

Special Issues

A journal special issue will be proposed to authors with accepted papers for submitting an extended version of their work.

More Information

Easychair platform for submissions: