Benoît Macq (UCLouvain) - "Coalitional Active Learning"
Many recent reports and articles show a big gap between the successful achievements of deep learning and their adoption in clinical practice. We propose a new implementation paradigm to overcome this gap.
Practitioners are committed to continuing medical education for their accreditation. They provide annotations and second advice in informal coalitions. These coalitions are also the place for continuing medical education.
We propose Coalitional Active Learning, which takes labels provided by the coalition instead of those only provided by individual hospitals, as in classical federated learning approaches. This will align the members on common best clinical practices and increase transfer knowledge between hospitals.
Coalitional Active Learning will provide a continuously joint improvement of the accuracy of the model and of the human expertise.
It is based on active learning, which relies on a parsimonious label harvesting on the most informative cases. For those cases, the data will go through an optimal sampler to the coalition. Experts will then provide labels and this will be used to make the clinical decision and to update the model. The labelling process increases the level of expertise of the practitioner who did the labelling. Coalitional active learning optimises this co-learning under the constraint of time-budgets.
We propose two veins to reach this goal. One based on an analytical approach by defining a coalitional gain and the related optimum sampling in the coalition and one based on stochastic scheduling.
We will also propose a new secure multicast of image data in a coalition across hospitals with guarantees of ephemeral use of the data. Tokenisation and anonymity of the labels are essential for the acceptance by the practitioners.