Script: 

selectAnnotations();
addPixelClassifierMeasurements("Tumour vs stroma", "Tumour vs stroma")


-----------------------------------------------------------------------------------------


Pixel classifier Tumor vs stroma:

{
  "metadata": {
    "inputPadding": 0,
    "inputResolution": {
      "pixelWidth": {
        "value": 1.7667454340672688,
        "unit": "µm"
      },
      "pixelHeight": {
        "value": 1.7667454340672688,
        "unit": "µm"
      },
      "zSpacing": {
        "value": 1.0,
        "unit": "z-slice"
      },
      "timeUnit": "SECONDS",
      "timepoints": []
    },
    "inputWidth": 512,
    "inputHeight": 512,
    "inputNumChannels": 3,
    "outputType": "CLASSIFICATION",
    "outputChannels": [
      {
        "name": "Stroma",
        "color": -8745193
      },
      {
        "name": "Tumour",
        "color": -9490209
      }
    ],
    "classificationLabels": {
      "0": {
        "name": "Stroma",
        "colorRGB": -3342337
      },
      "1": {
        "name": "Tumour",
        "colorRGB": -9490209
      }
    }
  },
  "op": {
    "type": "data.op.channels",
    "colorTransforms": [
      {
        "channelName": "Red"
      },
      {
        "channelName": "Green"
      },
      {
        "channelName": "Blue"
      }
    ],
    "op": {
      "type": "op.core.sequential",
      "ops": [
        {
          "type": "op.core.sequential",
          "ops": [
            {
              "type": "op.core.split-merge",
              "ops": [
                {
                  "type": "op.filters.multiscale",
                  "features": [
                    "GAUSSIAN"
                  ],
                  "sigmaX": 1.0,
                  "sigmaY": 1.0
                },
                {
                  "type": "op.filters.multiscale",
                  "features": [
                    "GAUSSIAN"
                  ],
                  "sigmaX": 2.0,
                  "sigmaY": 2.0
                },
                {
                  "type": "op.filters.multiscale",
                  "features": [
                    "GAUSSIAN"
                  ],
                  "sigmaX": 4.0,
                  "sigmaY": 4.0
                }
              ]
            },
            {
              "type": "op.ml.feature-preprocessor",
              "preprocessor": {
                "normalizer": {
                  "offsets": [
                    0.0,
                    0.0,
                    0.0,
                    0.0,
                    0.0,
                    0.0,
                    0.0,
                    0.0,
                    0.0
                  ],
                  "scales": [
                    1.0,
                    1.0,
                    1.0,
                    1.0,
                    1.0,
                    1.0,
                    1.0,
                    1.0,
                    1.0
                  ],
                  "missingValue": 0.0
                },
                "inputLength": 9,
                "outputLength": 9
              }
            }
          ]
        },
        {
          "type": "op.ml.opencv-statmodel",
          "model": {
            "class": "ANN_MLP",
            "statmodel": {
              "opencv_ml_ann_mlp": {
                "format": 3,
                "layer_sizes": [
                  9,
                  2
                ],
                "activation_function": "SIGMOID_SYM",
                "f_param1": 1.0,
                "f_param2": 1.0,
                "min_val": -9.4999999999999996e-01,
                "max_val": 9.4999999999999996e-01,
                "min_val1": -9.7999999999999998e-01,
                "max_val1": 9.7999999999999998e-01,
                "training_params": {
                  "train_method": "RPROP",
                  "dw0": 1.0000000000000001e-01,
                  "dw_plus": 1.2000000000000000e+00,
                  "dw_minus": 5.0000000000000000e-01,
                  "dw_min": 1.1920928955078125e-07,
                  "dw_max": 50.0,
                  "term_criteria": {
                    "epsilon": 1.0000000000000000e-02,
                    "iterations": 1000
                  }
                },
                "input_scale": [
                  5.2741671550416411e-02,
                  -1.1463322145237823e+01,
                  4.4396821070724825e-02,
                  -8.0112286466855611e+00,
                  7.1046781520769833e-02,
                  -1.4699234121210621e+01,
                  7.3536278283017981e-02,
                  -1.5981545862954265e+01,
                  5.5817793285706900e-02,
                  -1.0071510732920837e+01,
                  1.0045817467322164e-01,
                  -2.0782936935121128e+01,
                  9.4405880129716499e-02,
                  -2.0514497331162495e+01,
                  6.4797633004168298e-02,
                  -1.1692209967105608e+01,
                  1.2639511975888446e-01,
                  -2.6148311787538834e+01
                ],
                "output_scale": [
                  1.0,
                  0.0,
                  1.0,
                  0.0
                ],
                "inv_output_scale": [
                  1.0,
                  0.0,
                  1.0,
                  0.0
                ],
                "weights": [
                  [
                    -5.8303761392409514e-01,
                    1.0101562791966006e+00,
                    7.5734384662926901e-01,
                    -2.7994046497318609e-01,
                    -1.4056708482771693e-01,
                    -4.5490346304542251e-01,
                    -7.6180330111217343e-01,
                    1.0687488732716979e+00,
                    -6.6581641354401833e-01,
                    -6.5130209831879848e-01,
                    -4.4383502024941784e-01,
                    9.7464785829225553e-01,
                    1.7550302131018132e+00,
                    -8.6436256022431701e-01,
                    3.4350174966923968e+00,
                    -4.4331599292863144e+00,
                    4.0983026141139439e-01,
                    -7.8660812985863171e-01,
                    1.8844117115558163e+00,
                    -3.1231374414552548e+00
                  ]
                ]
              }
            }
          },
          "requestProbabilities": false
        },
        {
          "type": "op.core.convert",
          "pixelType": "UINT8"
        }
      ]
    }
  }
}