Machine learning (ML) systems are achieving remarkable performances at the cost of increased complexity. Hence, they become less interpretable, which may cause distrust. As these systems are pervasively being introduced to critical domains, such as medical image computing and computer assisted intervention (MICCAI), it becomes imperative to develop methodologies to explain their predictions. Such methodologies would help physicians to decide whether they should follow/trust a prediction or not. Additionally, it could facilitate the deployment of such systems, from a legal perspective. Ultimately, interpretability is closely related with AI safety in healthcare.

However, there is very limited work regarding interpretability of ML systems among the MICCAI research. Besides increasing trust and acceptance by physicians, interpretability of ML systems can be helpful during method development. For instance, by inspecting if the model is learning aspects coherent with domain knowledge, or by studying failures. Also, it may help revealing biases in the training data, or identifying the most relevant data (e.g., specific MRI sequences in multi-sequence acquisitions). This is critical since the rise of chronic conditions has led to a continuous growth in usage of medical imaging, while at the same time reimbursements have been declining. Hence, improved productivity through the development of more efficient acquisition protocols is urgently needed.

This workshop aims at introducing the challenges & opportunities related to the topic of interpretability of ML systems in the context of MICCAI.

Interpretability can be defined as an explanation of the machine learning system. It can be broadly defined as global, or local. The former explains the model and how it learned, while the latter is concerned with explaining individual predictions. Visualization is often useful for assisting the process of model interpretation. The model’s uncertainty may be seen as a proxy for interpreting it, by identifying difficult instances. Still, although we can find some approaches for tackling machine learning interpretability, there is a lack of formal and clear definition and taxonomy, as well as general approaches. Additionally, interpretability results often rely on comparing explanations with domain knowledge. Hence, there is need for defining objective, quantitative, and systematic evaluation methodologies.

Covered topics include but are not limited to:

The program of the workshop includes keynote presentations of experts working in the field of interpretability of machine learning. A selection of submitted manuscripts will be chosen for short oral presentations (12 minutes + 3 minutes Q&A) alongside the keynotes. Finally, we will have a group discussion which leaves room for a brainstorming on the most pressing issues in interpretability of machine intelligence in the context of MICCAI.

The preliminary half-day program is:

Authors should prepare a manuscript of 8 pages, including references. The manuscript should be formatted according to the Lecture Notes in Computer Science (LNCS) style. All submissions will be reviewed by 3 reviewers. The reviewing process will be single-blinded. Authors will be asked to disclose possible conflict of interests, such as cooperation in the previous two years. Moreover, care will be taken to avoid reviewers from the same institution as the authors. The selection of the papers will be based on their relevance for medical image analysis, significance of results, technical and experimental merit, and clear presentation.

We intend to join the MICCAI Satellite Events joint proceedings, and publish the accepted papers as LNCS. We are also considering making the pre-print of the accepted papers publicly available.

Click here to submit your paper.


We thank our sponsors.