Keywords: AI Diffusion Models, White Matter, WMH segmentation, Domain Generalization, Normalization
Motivation: Self-adaptation normalization (SAN) method generalizes lesion segmentation to new sites by using a linear generator to convert inputs to a site-independent style. But it fails when the domain difference is mostly nonlinear.
Goal(s): Develop a semi-nonlinear self-adaptation network(SNSAN) to generalize the White Matter Hyperintensity(WMH) segmentation model to an external site.
Approach: To replace the linear generator, the method blends two SAN results with a pseudo correlation map, later use the gradient reversal method to guide the result to a site-unrelated style.
Results: SNSAN normalizes the input data close to a Gaussian distribution and improves the generalization performance on the data from external site.
Impact: We provide a simple and efficient semi-nonlinear normalization method to enhance the domain generalization, and its performance is better than SAN when the domain gap is affected by more nonlinear factors.
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