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CherBass authored May 4, 2022
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Expand Up @@ -124,7 +124,7 @@ Networks are compared using accuracy score and normalised cross correlation (NCC

Table 1: ICAM ablation experiments in the HCP dataset.

Table 1 (top) demonstrates performance of the network under ablation. Here, ICAM_DRIT represents a baseline re-implementation of the DRIT++ network (Lee et al., 2019) modified to support much more compact architecture (see Bass et al., 2020 for more details); ICAM_BCE reflects extension of DRIT++ integrating the classifier on the latent space and adding the rejection sampling module during training, ICAM_FA additionally integrates FA map regularisation, and ICAM represents the full network, which incorporates the L2 reconstruction loss. Each component of the network improves performance (either NCC+ or NCC-).
Table 1 (top) demonstrates performance of the network under ablation. Here, ICAM_DRIT represents a baseline re-implementation of the DRIT++ network (Lee et al., 2019) modified to support much more compact architecture (see Bass et al., 2020 for more details); ICAM_BCE reflects extension of DRIT++ integrating the classifier on the latent space and adding the rejection sampling module during training, ICAM_FA additionally integrates FA map regularisation, and ICAM represents the full network, which incorporates the L2 reconstruction loss. Each component of the network improves performance (either NCC+ or NCC-). We note that the BCE loss (including rejection sampling) improves of NCC (+) quite a lot which is harder as learning to add lesions is more difficult than removing them.

![lambda M experiments](lambda_sweep_experiments.png)
Table 2: FA map regularisation experiment (sweep of lambda M parameter) in the HCP dataset.
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