Dermoscopy Skin Lesion Malignancy Classification (PWDR) L1-517

Medical ImagingCross-polarized cutaneous reflectance imaging with melanoma / nevus categorical readoutδ=3 · standardL_DAG = 4.9📋 Stub — not mineable

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Unclaimed Principle — open for contribution

This Principle is declared in the catalog but has no reference solver, no pinned dataset, and is not registered on-chain. There is no reward pool. Submitting a cert against this Principle today will record the cert for reproducibility but pay zero PWM.

To claim it as a Bounty #7 contribution: open a PR adding (1) a reference solver, (2) ≥1 dataset pinned to IPFS, (3) updates to the L3 manifest with dataset CIDs. After verifier-agent triple-review, the founders' 3-of-5 multisig signs PWMRegistry.register() and the Principle becomes mineable.

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Forward model E

S t a g e 1 (s ib l in g t o L 1 - 049 r e f l ec t an ce - ima g in g cor e) : cr oss - p o l a r i z e d R GB d er m osco p y \to a sy mm e t r y, b or d er, co l or, s t r u c t u r e m or p h o l o g i c a l f e a t u r es . S t a g e 2 : T D S = 1.3 \cdot A + 0.1 \cdot B + 0.5 \cdot C + 0.5 \cdot D; c l a ss = s t e p (T D S a t [4.75, 5.45]); a l t er na t i v e 7 - p o in t s u m; a l t er na t i v e M e n z i es p os i t i v e / n e g a t i v e .

Dermoscopy Skin Lesion Malignancy Classification (PWDR): wraps L1-049 fundus analytical core (RGB cross-polarized reflectance imaging) — note: fundus L1-049 is geometrically retinal, but the physics core of cross-polarized RGB reflectance imaging applies to skin dermoscopy with appropriate scale and illumination adjustments — with the established ABCDE / 7-point checklist / Menzies dermoscopy clinical-grading rules. Stage 1 (analytical, from L1-049 sibling-physics): cross-polarized white-light dermoscopy recovers cutaneous lesion morphological features — asymmetry, border, color heterogeneity, differential structures (pigment network, dots, streaks, regression). Stage 2 (deterministic threshold): apply ABCDE TDS or 7-point checklist or Menzies criteria to assign categorical labels. Difficulty tier delta = 3. Mismatch parameters: dermoscope_polarization_efficiency, gel_immersion_uniformity, lesion_contrast_against_skin_background, hair_artifact, photographic_color_calibration, manual_feature_scoring_disagreement.

L-DAG

L.cross_polarized_illumination -> L.diffuse_reflectance -> L.detector_response -> L.morphological_feature_extraction -> L.abcde_threshold_classifier -> int.spatial

L.cross_polarized_illuminationL.diffuse_reflectanceL.detector_responseL.morphological_feature_extractionL.abcde_threshold_classifierint.spatial

Well-posedness W

Existence:: true
Uniqueness:: conditional
Stability:: conditional
κ:: 80

Existence and uniqueness conditional on adequate cross-polarization (suppresses skin glare) and sufficient lesion contrast against background skin. Stability inherits sibling-core kappa_eff plus additive contribution from manual_feature_scoring_disagreement (the dominant inter-rater variability source in clinical practice). Joint Hadamard well-posedness for the coupled dermoscopy + ABCDE/7-point/Menzies threshold forward established by Stolz 1994 (foundational ABCDE), Argenziano 1998 (7-point checklist), Menzies 1996 (Menzies method), Henning 2007 (CASH algorithm), Esteva 2017 (deep-learning benchmark).

Solvability C

Solver class:: linear-operator + convex optimisation [feature-segmentation + threshold] | end-to-end deep neural [DeepDerm, DermaSensor] with explicit physics-informed feature regularization
Convergence rate q:: 2
Complexity:: O(H * W * N_features) per stage; total stage-1-dominated

Specs (0)

No L2 specs registered yet for this principle.