# Visualization config for gliomasam3_moe # Output: vis_res/ (each visualization type in a separate subfolder) data: format: "segmamba_npz" root_dir: "/data/yty/brats23_segmamba_processed" npz_dir: "/data/yty/brats23_segmamba_processed" modalities: ["t1n", "t1c", "t2f", "t2w"] seg_name: "seg" predictions: ours: name: "GliomaSAM3-MoE" dir: "/root/githubs/gliomasam3_moe/vis_res/predictions_ours" type: "label" baselines: - name: "SegMamba" dir: "/root/githubs/SegMamba/prediction_results/segmamba_brats23_ep799" type: "segmamba_3c" aux_dir: "/root/githubs/gliomasam3_moe/vis_res/aux_cache" visualization: output_dir: "/root/githubs/gliomasam3_moe/vis_res" overlay_modality: "t1c" alpha: 0.45 colors: WT: [1.0, 0.85, 0.0] TC: [0.0, 1.0, 0.25] ET: [1.0, 0.0, 0.0] boundary_region: "ET" frag_bins: [1, 3, 5] scale_bins: [50, 200, 500] # Case selection cases: # 1) Main qualitative comparison (Ours vs SegMamba) qualitative: - "BraTS-GLI-00005-000" - "BraTS-GLI-00006-000" - "BraTS-GLI-00017-000" # 2) ET-absent case study (best candidate with highest pre/post diff) et_absent: - "BraTS-GLI-00012-000" # 3) Boundary-focused visualization boundary: - "BraTS-GLI-00005-000" - "BraTS-GLI-00017-000" # 4) Tiny/fragmented ET cases tiny_et: - "BraTS-GLI-00005-000" - "BraTS-GLI-00006-000" # 5) Cross-year robustness (placeholder - needs cross-year data) cross_year: {} # 6) MoE routing interpretability moe: - "BraTS-GLI-00018-000" - "BraTS-GLI-00012-000" # 7) Concept tokens interpretability concept_tokens: - "BraTS-GLI-00005-000" - "BraTS-GLI-00006-000" # 8) Dual-domain enhancement effect dual_domain: - "BraTS-GLI-00005-000" - "BraTS-GLI-00017-000" # 9) Augmentation robustness (AmpMix) ampmix: - base: "BraTS-GLI-00005-000" mix: "BraTS-GLI-00006-000" lam: 0.5 # 10) Failure cases / limitations failure: - "BraTS-GLI-00020-000" failure_notes: "BraTS-GLI-00020-000": "potential boundary ambiguity" # 11) Efficiency / inference visualization efficiency: case_id: "BraTS-GLI-00005-000" roi_size: [128, 128, 128] overlap: 0.5