Rami Eisawy |
Image-Based Biomedical Modeling Group (IBBM)
deepc AI Platform
Office: External, Werk1
Email: firstname.lastname@example.org email@example.com
Given the scenario where a lack of labelled data exists, develop an model that is able to detect a range of pathologies (not limited to hyperintensities). A single imaging sequence will be selected (i.e. FLAIR). Current state of the art methods rely on autoencoder reconstruction based pipelines, however clinical success is limited here and this project would focus on supplementing and/or improving this approach.
A single algorithm/model has the potential to be deployed to multiple sites either via site-specific calibration and/or domain adaptation. This provides a favourable environment for clients in terms of performance, privacy and data ownership. However, there is the possibility to leverage information across all installations in the form of distributed training. In this setting, we can consider these various site installations as edge devices. The challenge remains in producing a global (server) model that is robust and efficient in terms of its aggregation scheme, tackles the heterogeneous nature of the nodes and whether or not the difficulty in data distribution across clients can be addressed at the server level to lead to a more accurate model. The focus of the project will be the aggregation scheme. Recent projects have tackled this by simply using an average function (FedAvg), personalisation layers (FedPer) and matched averaging (FedMA) to name a few. An internal CT brain model will be provided and ideally, the (Clara Train SDK) is utilised. Previous experience in another framework that supports custom components/models would also be acceptable.
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|Languages:||English, German, Arab|
|Groups:||Image-Based Biomedical Modeling|
|Expertise:||Segmentation, Medical Imaging|