Project AHS-SRN - Architecture and Hyperparameter Search for Super-Resolution Networks Operating on Medical Images
Project partners
Project description
Diagnosis of pathologies in the human respiratory system have recently included results of computational fluid dynamics (CFD) simulations, which allow numerical quantification of respiratory flows by the pressure loss, the temperature distribution, etc. Highly resolved computational meshes based on computer tomography (CT) images are necessary to accurately simulate the respiratory flow. However, clinical CT image resolution is often limited by radiation dose and acquisition time. Super-resolution networks (SRNs) have the potential to increase the resolution of images a posterior the recording. In this project, SRNs are employed and optimized to recover high-resolution (HR) from low-resolution (LR) CT images. SRNs predictions are validated by comparing the results of CFD simulations, carried out with the highly scalable lattice-Boltzmann method (LBM).
The performance of SRNs is highly dependent on the hyperparameters, either related to model architecture or optimizer. Finding the optimal architectures and hyperparameters is limited by computational resources as the search space is often too large to explore exhaustively. The DeepHyper (DH) framework aims to tackle these challenges by employing an asynchronous Bayesian optimization (BO) approach for hyperparameter and architecture search at HPC scale. Another limitation of existing SRNs is that they provide forecasts without any uncertainty estimates. In this project, the developers will build on DeepHyper’s automated deep ensemble for uncertainty quantification capability (AutoDEUQ). DeepHyper/AutoDEUQ estimates aleatoric and epistemic uncertainties by: automatically generating a catalog of neural networks models through joint neural architecture and hyperparameter search, wherein each model is trained to model the distribution of the data; and selecting a set of high-performing models to construct the ensembles, and estimating aleatoric and epistemic uncertainties from the generated model ensembles.
It is the aim of the proposed cooperation to investigate architecture and hyperparameter search algorithms for SRNs for enhancing resolution of CT images. This includes performance, scalability, and accuracy analyses of DH. The findings will be juxtaposed to those obtained employing similar tools for distributed hyperparameter optimization such as Ray Tune. JSC brings in its knowledge about SRNs, medical data, and CFD simulations, and ANL contributes with its expertise in architecture and hyperparameter search in general, and in employing DH on HPC systems.
Links
Project website at JLESC: AHS-SRN