Impact Score 9.07
Call for Papers
Medical Image Analysis Special Issue on
Explainable and Generalizable Deep Learning Methods for Medical Image Computing
Deep learning has recently revolutionized the methods used for medical image computing due to automated feature discovery and superior results. However, they have significant limitations that make clinicians skeptical on their usefulness for clinical practice. Deep learning models are essentially black boxes that do not offer explainability of their decision-making process which in turn makes it hard to debug them when necessary. The poor explainability leads to distrust from clinicians who are trained to make explainable clinical inferences. In addition, their generalizability is still limited in clinical environments due to the many different imaging protocols, large variations in image-based manifestation of pathologies and rare diseases whose related data may have not been used during training. The generalizability problem becomes even more conspicuous when a deep learning model trained on data from a given medical center is deployed to other medical centers whose data have significant variations or there is a domain shift from the training set. Consequently, there is an urgent need for innovative methodologies to improve the explainability and generalizability of deep learning methods that will enable them to be used routinely in clinical practice.
Topics of Interest
To address the limitations of deep learning methods in medical image computing, this special issue solicits novel explainable/interpretable and generalizable deep learning methods for intelligent medical image computing applications. The methods should provide novel explainable/interpretable and generalizable solutions to key application domains such as disease classification and prediction, pathology detection and segmentation, image registration and reconstruction. Topics of interest include, but not limited to the following:
Accepted papers should demonstrate the improvements offered by their methods compared to previous deep learning methods.
Paper submission deadline: December 1, 2021.
1st round of Reviews: Feb 1, 2022.
Revised manuscript due: April 15, 2022.
Final decision: May 15, 2022.
Camera ready version: June 15, 2022.
Guotai Wang, PhD. University of Electronic Science and Technology of China. [email protected]
Shaoting Zhang, PhD. The University of North Carolina at Charlotte, and SenseTime Research.
Tom Vercauteren, PhD. King’s College London.
Dimitris Metaxas, PhD. Rutgers University.