Keywords: Analysis/Processing, Reproductive, Data Processing, Analysis/Processing, AI/ML Image Reconstruction
Motivation: Most commercially available deep-learning tools are black-box models, where additional training is not possible. This may hamper the performance for unseen-data. This work proposes novel harmonization concept for this black-box model.
Goal(s): We propose a harmonization pipeline, BboxHarmony, that trains a harmonization network for a black-box model.
Approach: First, randomly perturbed images were evaluated for the black-box model and the images with high performance results (pseudo-target domain images) were used to train the harmonization network. Then, the network was refined for black-box model using zeroth-order optimization that approximates backpropagation.
Results: BboxHarmony successfully created harmonized images that provided high performance for the black-box model.
Impact: BboxHarmony proposes a novel concept of harmonizing data for a black-box model and may have an important impact in the real-world where most commercial networks are black-box.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords