L1 ⚛ L1-517 ⊙ Testnet

Dermoscopy Skin Lesion Malignancy Classification (PWDR)

Medical Imaging · Cross-polarized cutaneous reflectance imaging with melanoma / nevus categorical readout

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⬇ principle.md ⬇ principle.json

Fork L1-517

⬇ L1-517.md ⬇ L1-517.json

Prompt — copy into your LLM

You are helping me submit a MODIFICATION of L1-517 (Dermoscopy Skin Lesion Malignancy Classification (PWDR)) to the PWM Protocol — a Principle (L1) artifact.

I will paste a Markdown template (or the current L1-517.md).
1. Rewrite the Markdown so the science is correct and clearly explained for my change.
2. Regenerate the sibling L1-517.json so EVERY field matches the Markdown.
3. Keep the schema in the "File Mapping" footer at the bottom of the MD.
4. Keep the parent reference unchanged unless I ask otherwise.
Rules: the Markdown is the source of truth; use SI units; do NOT invent benchmark scores.
Output each file in its own fenced code block tagged with the filename.

Here is my template:
[PASTE THE .md HERE]

⚙ Forward Model

y = ∫_A dA `L.abcde_threshold_classifier` `L.morphological_feature_extraction` `L.detector_response` `L.diffuse_reflectance` `L.cross_polarized_illumination` x + n,    n ~ 𝒩(0, σ²)

world state x (1D lesion class)

→

L · cross polarized illumination `L.cross_polarized_illumination`

→

L · diffuse reflectance `L.diffuse_reflectance`

→

L · detector response `L.detector_response`

→

L · morphological feature extraction `L.morphological_feature_extraction`

→

L · abcde threshold classifier `L.abcde_threshold_classifier`

→

Spatial integration ∫_A dA

→

observation y (photon detector)

Noise: additive Gaussian noise

Markdown — human-readable source of truth

⚙ auto-generated

⬇ L1-517.md

# ⚛ L1 Principle — Dermoscopy Skin Lesion Malignancy Classification (PWDR) **ID:** `L1-517` · **Status:** ⊙ Testnet (genesis catalog) > **🌐 Domain:** Medical Imaging — *Cross-polarized cutaneous reflectance imaging with melanoma / nevus categorical readout* > **🎯 Problem class:** linear inverse with categorical readout · **🧮 Solution space:** 1D lesion class > **📡 Carrier:** photon · **🌫 Noise:** gaussian > **⚖ Difficulty (δ):** 3 · **⛓ Block:** 41553387 --- ## 🧠 1. Introduction **Dermoscopy Skin Lesion Malignancy Classification (PWDR)** is a **linear inverse with categorical readout** whose unknown lives in **1D lesion class** space, within the **Cross-polarized cutaneous reflectance imaging with melanoma / nevus categorical readout** sub-domain of **Medical Imaging**. Measurements consist of photons collected by an optical detector via a **cross polarized dermoscopy with abcde grading** sensing mechanism. The forward operator applies, in order: L · cross polarized illumination operator; L · diffuse reflectance operator; L · detector response operator; L · morphological feature extraction operator; L · abcde threshold classifier operator; pixel-level spatial averaging on the detector. Observations are corrupted by additive Gaussian noise. 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). ## ⚙ 2. Forward Model Physical chain: **x** → L · cross polarized illumination → L · diffuse reflectance → L · detector response → L · morphological feature extraction → L · abcde threshold classifier → Spatial integration → **y** (detector). ``` y = ∫_A dA `L.abcde_threshold_classifier` `L.morphological_feature_extraction` `L.detector_response` `L.diffuse_reflectance` `L.cross_polarized_illumination` x + n, n ~ 𝒩(0, σ²) ``` **Measurement DAG:** | Primitive | What it does | |---|---| | `L.cross_polarized_illumination` | L · cross polarized illumination operator | | `L.diffuse_reflectance` | L · diffuse reflectance operator | | `L.detector_response` | L · detector response operator | | `L.morphological_feature_extraction` | L · morphological feature extraction operator | | `L.abcde_threshold_classifier` | L · abcde threshold classifier operator | | `int.spatial` | Pixel-level spatial averaging on the detector | ## 🔬 3. Physics Fingerprint | Property | Value | |---|---| | Domain | Medical Imaging | | Sub domain | Cross-polarized cutaneous reflectance imaging with melanoma / nevus categorical readout | | Carrier | photon | | Problem class | linear_inverse_with_categorical_readout | | Solution space | 1D_lesion_class | | Noise model | gaussian | | Integration axis | spatial | | Difficulty delta | 3 | | L dag | 4.9 | ## 📡 4. Measurement Model 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). | Metric | Value | |---|---| | Metric | categorical_accuracy | | Secondary | sensitivity_for_melanoma | ## 📏 5. Operating Range (Ω) **Center problem class:** `dermoscopy_abcde_pwdr` · **Forward operator:** `dermoscopy_pwdr_forward` **Center point:** | Parameter | Unit | Value | |---|---|---| | H | px | 2048 | | W | px | 3072 | | Snr db | dB | 32 | | Hair artifact | — | 0 | | Magnification | — | 20 | | Polarization state | — | cross_polarized | | Pixel resolution um | µm | 50 | | Gel immersion uniformity | — | 0 | | Photographic color calibration | — | 0 | | Dermoscope polarization efficiency | — | 0 | | Manual feature scoring disagreement | — | 0 | | Lesion contrast against skin background | — | 0 | **Allowed bounds:** | Parameter | Unit | Range | |---|---|---| | H | px | 512 – 6000 | | W | px | 512 – 6000 | | Magnification | — | 10 – 50 | | Pixel resolution um | µm | 20 – 200 | ## 🎯 6. Tolerance (ε) **Center tolerance:** 0.85_accuracy | Metric | Range | |---|---| | Categorical accuracy | 0.5 – 0.99 | ## ⚖ 7. Hardness Function Hardness scales as **`epsilon_fn`** on **categorical_accuracy**, with κ = `80` and δ = `3`. ## 💾 8. Reference Dataset - **primary** · weight 1.0 · IPFS _(not pinned yet)_ ## 9. On-chain Registration - **Chain hash:** `0xeea451afa0e64d2ebb58f3b05ab68fff663dc89008dc10053c11b8e4d684fe46` - **Chain tx hash:** `0xefea66fa44c7424ab1e07799504d3f567dce75c90d49990969d26b16b279c8f3` - **Chain block:** `41553387` --- ## File Mapping This bundle consists of: `L1-517.md`, `L1-517.json`. | File | Role | How to regenerate | |------|------|-------------------| | `L1-517.md` | Source of truth — edit this | Human or LLM | | `L1-517.json` | Structured metadata for the registry | LLM regenerates from the sections above | **Prompt for your LLM after editing this Markdown:** > Read the attached Markdown. Regenerate the sibling `.json` so every field matches. > Preserve the schema documented in the rows above. > Output each file in its own fenced code block tagged with the filename. > Output only the JSON object. _This Markdown was auto-synthesized from the catalog row for `L1-517`._ _Edit it, regenerate the JSON, and submit at [/submit](/submit) to claim the artifact._

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L2-517-001 Dermoscopy Skin Lesion Malignancy Classification (PWDR) — Spec Skeleton

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📋 JSON metadata

{
  "artifact_id": "L1-517",
  "chain_block": 41553387,
  "chain_hash": "0xeea451afa0e64d2ebb58f3b05ab68fff663dc89008dc10053c11b8e4d684fe46",
  "chain_tx_hash": "0xefea66fa44c7424ab1e07799504d3f567dce75c90d49990969d26b16b279c8f3",
  "domain": "Medical Imaging",
  "hardness_fn": {
    "delta": 3,
    "kappa": 80,
    "metric": "categorical_accuracy",
    "type": "epsilon_fn"
  },
  "initiator_dataset": [
    {
      "ipfs_cid": null,
      "license_hash": null,
      "name": "primary",
      "weight": 1.0
    }
  ],
  "layer": "L1",
  "observable_profile": {
    "metric": "categorical_accuracy",
    "regime": "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).",
    "secondary": "sensitivity_for_melanoma"
  },
  "physics_fingerprint": {
    "L_DAG": 4.9,
    "carrier": "photon",
    "difficulty_delta": 3,
    "domain": "Medical Imaging",
    "integration_axis": "spatial",
    "noise_model": "gaussian",
    "primitives": [
      "L.cross_polarized_illumination",
      "L.diffuse_reflectance",
      "L.detector_response",
      "L.morphological_feature_extraction",
      "L.abcde_threshold_classifier",
      "int.spatial"
    ],
    "problem_class": "linear_inverse_with_categorical_readout",
    "sensing_mechanism": "cross_polarized_dermoscopy_with_abcde_grading",
    "solution_space": "1D_lesion_class",
    "sub_domain": "Cross-polarized cutaneous reflectance imaging with melanoma / nevus categorical readout",
    "title": "Dermoscopy Skin Lesion Malignancy Classification (PWDR)"
  },
  "size_tiers": {
    "allowed_forward_operators": [
      "dermoscopy_pwdr_forward",
      "dermoscopy_abcde_forward",
      "dermoscopy_7point_forward",
      "dermoscopy_menzies_forward",
      "dermoscopy_cash_forward"
    ],
    "allowed_omega_dimensions": [
      "H",
      "W",
      "magnification",
      "pixel_resolution_um",
      "polarization_state",
      "SNR_dB",
      "dermoscope_polarization_efficiency",
      "gel_immersion_uniformity",
      "lesion_contrast_against_skin_background",
      "hair_artifact",
      "photographic_color_calibration",
      "manual_feature_scoring_disagreement"
    ],
    "allowed_problem_classes": [
      "dermoscopy_abcde_pwdr",
      "dermoscopy_7point_pwdr",
      "dermoscopy_menzies_pwdr",
      "dermoscopy_cash_pwdr",
      "dermoscopy_combined_classifier"
    ],
    "center_spec": {
      "epsilon_fn_center": "0.85_accuracy",
      "forward_operator": "dermoscopy_pwdr_forward",
      "input_format": "rgb_dermoscopy_with_lesion_label",
      "omega": {
        "H": 2048,
        "SNR_dB": 32,
        "W": 3072,
        "dermoscope_polarization_efficiency": 0.0,
        "gel_immersion_uniformity": 0.0,
        "hair_artifact": 0.0,
        "lesion_contrast_against_skin_background": 0.0,
        "magnification": 20,
        "manual_feature_scoring_disagreement": 0.0,
        "photographic_color_calibration": 0.0,
        "pixel_resolution_um": 50,
        "polarization_state": "cross_polarized"
      },
      "problem_class": "dermoscopy_abcde_pwdr"
    },
    "epsilon_bounds": {
      "categorical_accuracy": [
        0.5,
        0.99
      ]
    },
    "omega_bounds": {
      "H": [
        512,
        6000
      ],
      "W": [
        512,
        6000
      ],
      "magnification": [
        10,
        50
      ],
      "pixel_resolution_um": [
        20,
        200
      ]
    }
  },
  "staked_pwm": 0.0,
  "status": "testnet",
  "sub_domain": "Cross-polarized cutaneous reflectance imaging with melanoma / nevus categorical readout",
  "title": "Dermoscopy Skin Lesion Malignancy Classification (PWDR)"
}

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