Interpretable and Annotation-Efficient Learning for Medical Image Computing

Dear Colleagues,

As data-hungry methods continue to drive advancements in medical imaging, the need for high-quality annotated data to train and validate these methods continues to grow. Further, with the pressing need to address health disparities and to prevent learned systems from internalizing biases, there has never been a greater need for thorough study and discussion of best practices in data collection and annotation.

Additionally, the remarkable performances achieved by current machine learning systems are achieved at the cost of opacity and often contain training-data-induced bias, causing distrust and potentially limiting clinical acceptance. As these systems are pervasively being introduced to critical domains, such as medical image computing and computer-assisted intervention, it becomes imperative to develop methodologies allowing insight into their decision making. Such methodologies would help physicians to decide whether they should follow and trust automatic decisions. Additionally, interpretable machine learning methods could facilitate defining the legal and ethical framework of their clinical deployment.

For this Special Issue, we invite the authors of the very best works of iMIMIC and LABELS Workshops at MICCAI 2020 to submit a substantially extended and revised version of their workshop paper. Each extended submission to this Special Issue should contain at least 50% of new material, e.g., in the form of technical extensions, more in-depth evaluations, or additional use cases and a change of title, abstract, and keywords.

This special issue is also open to new submissions that are in line with the themes of the two workshops and with special emphasis on medical imaging: interpretability and model visualization techniques, local and textual explanations, uncertainty quantification, label crowdsourcing and validation, data augmentation and active learning, domain adaptation and transfer learning, modeling label uncertainty and training in the presence of noise.

All submissions will undergo a peer-review process according to the journal's rules of action. At least two technical committees will act as reviewers for each extended article submitted to this Special Issue; if needed, additional external reviewers will be invited to guarantee a high-quality reviewing process.

Prof. Dr. Jaime Cardoso
Mr. Nicholas Heller
Prof. Dr. Pedro Henriques Abreu
Prof. Dr. Ivana Išgum
Prof. Dr. Diana Mateus
Guest Editors

Manuscript Submission Information

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