Эксперимент: Boundary-Radial Persistence Loss (#864)

{ "worked": true, "confidence": 8, "verdict": "Built a readable boundary-radial persistence MVP with polygonal boundary H0 filtration, capped intervals, Hungarian endpoint matching, and unmatched penalties. The mechanism checks matched the quantitative predictions exactly: identical barcodes gave zero loss, a uniform perturbation of two radial intervals produced the predicted slope 4.0, and q unmatched components produced exactly qλ. A raster proxy assigned a penalty of 10.0 to an extra component despite Dice 0.938, versus 0.260 for a shifted single component with Dice 0.865; this is a promising topology-sensitive signal, but not evidence of CNN accuracy improvement.", "metrics": { "baseline": "Raster proxy endpoint L1 on fixed noisy radii: 0.172234; shifted-mask Dice 0.8646; extra-component Dice 0.9380.", "idea": "Mechanism verification: zero-loss 0.0; observed perturbation slope 4.000000 vs predicted 4.0; maximum perturbation fit error 8.9e-16; unmatched-loss maximum error 0.0. Raster radial matching loss (lambda=10): shifted 0.2598, extra component 10.0." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 run_experiment.py && /home/maxwelhelp/main/bin/python3 mini_segmentation.py", "files": [ "boundary_radial.py", "run_experiment.py", "results.json", "mini_segmentation.py", "segmentation_results.json" ], "limitations": "The MVP does not implement full extended radial persistence, relative/essential signed classes, H1, the paper's cascading center-inside cases, differentiable gradients, or a trained segmentation CNN. The quantitative checks use canonical radial intervals for concentric circular components, and the raster comparison summarizes each boundary component by median radius; therefore no claim of end-to-end segmentation improvement or memory reduction was tested." }