Advances in Weak Supervision for Medical Image Analysis

Dear Coleagues,

Recent advances in machine and deep learning have revealed significant progress in multiple medical image analysis tasks, such as registration, reconstruction, classification, etc. Most approaches require the collection of a large amount of annotated data and their performance notably decreases when only a small amount of data are available or when the data prove to be heterogenous, unbalanced, or incomplete. Moreover, the process of annotation is tedious and complex, even for experts (medical doctors for instance), and this limits the accumulation of good-quality data.

To address these issues, a promising strategy consists of developing machine learning models that have a good performance in solving image analysis problems, but with a partial annotation of data, leading to the weak supervision of the learning phase.

The purpose of this Special Issue is to collect and diffuse recent advances in research proposing novel solutions to face these challenges. These can concern the development of weak supervised machine learning models for medical image analysis as well as other related subjects, such as the improvement of data quality, efficient strategies for annotating data, and research about the explainability of deep learning architectures. This latter topic is of high importance for creating weak supervised models, since it permits us to understand the capacities of fully supervised models according to datasets (the importance of features, classification errors, incorrect data annotation, etc.) and potentially reduce their need for supervision.  

Prof. Dr. Antoine Vacavant
Prof. Dr. Anna Fabijańska
Guest Editors

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