Implemented MISmetric

README.md

@@ -27,8 +27,10 @@ The open-source and free to use Python package miseval was developed to establis
 | ----------- | ----------- | ----------- |
 | Dice Similarity Index | "DSC", "Dice", "DiceSimilarityCoefficient" | miseval.calc_DSC() |
 | Intersection-Over-Union | "IoU", "Jaccard", "IntersectionOverUnion" | miseval.calc_IoU() |
+| MISmetric | "MISm", "MISmetric" | miseval.calc_MISm() |
 | Sensitivity | "SENS", "Sensitivity", "Recall", "TPR", "TruePositiveRate" | miseval.calc_Sensitivity() |
 | Specificity | "SPEC", "Specificity", "TNR", "TrueNegativeRate" | miseval.calc_Specificity() |
+| Weighted Specificity | "mSPEC", "WeightedSpecificity" | miseval.calc_Specificity_Weighted() |
 | Precision | "PREC", "Precision" | miseval.calc_Precision() |
 | Accuracy | "ACC", "Accuracy", "RI", "RandIndex" | miseval.calc_Accuracy() |
 | Balanced Accuracy | "BACC", "BalancedAccuracy" | miseval.calc_BalancedAccuracy() |

miseval/__init__.py

@@ -51,6 +51,8 @@
 from miseval.volumetric_similarity import *
 # Matthews Correlation Coefficient
 from miseval.mcc import *
+# MISmetric
+from miseval.mism import *
 
 #-----------------------------------------------------#
 #         Access Functions to Metric Functions        #
@@ -95,6 +97,8 @@
     "Specificity": calc_Specificity,
     "TNR": calc_Specificity,
     "TrueNegativeRate": calc_Specificity,
+    "wSPEC": calc_Specificity_Weighted,
+    "WeightedSpecificity": calc_Specificity_Weighted,
     "PREC": calc_Precision,
     "Precision": calc_Precision,
     "VS": calc_VolumetricSimilarity,
@@ -108,7 +112,9 @@
     "MCC_normalized": calc_MCC_Normalized,
     "nMCC": calc_MCC_Normalized,
     "MCC_absolute": calc_MCC_Absolute,
-    "aMCC": calc_MCC_Absolute
+    "aMCC": calc_MCC_Absolute,
+    "MISm": calc_MISm,
+    "MISmetric": calc_MISm
 }
 
 #-----------------------------------------------------#

miseval/mism.py

@@ -0,0 +1,48 @@
+#==============================================================================#
+#  Author:       Dominik Müller                                                #
+#  Copyright:    2022 IT-Infrastructure for Translational Medical Research,    #
+#                University of Augsburg                                        #
+#                                                                              #
+#  This program is free software: you can redistribute it and/or modify        #
+#  it under the terms of the GNU General Public License as published by        #
+#  the Free Software Foundation, either version 3 of the License, or           #
+#  (at your option) any later version.                                         #
+#                                                                              #
+#  This program is distributed in the hope that it will be useful,             #
+#  but WITHOUT ANY WARRANTY; without even the implied warranty of              #
+#  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the               #
+#  GNU General Public License for more details.                                #
+#                                                                              #
+#  You should have received a copy of the GNU General Public License           #
+#  along with this program.  If not, see <http://www.gnu.org/licenses/>.       #
+#==============================================================================#
+#-----------------------------------------------------#
+#                   Library imports                   #
+#-----------------------------------------------------#
+# External modules
+import math
+# Internal modules
+from miseval.confusion_matrix import calc_ConfusionMatrix
+from miseval import calc_DSC_Sets, calc_Specificity_Weighted
+
+#-----------------------------------------------------#
+#                Calculate : MISmetric                #
+#-----------------------------------------------------#
+"""
+Combination of weighted Specificity for p=0 and Dice Similarity Coefficient
+as Backbone for p>0.
+
+Recommended for weak-labeled datasets.
+
+References:
+    Coming soon.
+"""
+def calc_MISm(truth, pred, c=1, alpha=0.1):
+    # Obtain confusion mat
+    tp, tn, fp, fn = calc_ConfusionMatrix(truth, pred, c)
+    # Identify metric wing
+    p = tp + fn
+    # Compute & return normal dice if p > 0
+    if p > 0: return calc_DSC_Sets(truth, pred, c)
+    # Compute & return weighted specificity if p = 0
+    else : return calc_Specificity_Weighted(truth, pred, c, alpha)

miseval/specificity.py

@@ -49,3 +49,15 @@ def calc_Specificity_CM(truth, pred, c=1):
     else : spec = 0.0
     # Return specificity
     return spec
+
+#-----------------------------------------------------#
+#           Calculate : Weighted Specificity          #
+#-----------------------------------------------------#
+def calc_Specificity_Weighted(truth, pred, c=1, alpha=0.1):
+    # Obtain confusion mat
+    tp, tn, fp, fn = calc_ConfusionMatrix(truth, pred, c)
+    # Compute weighted specificity
+    if (fp+tn) != 0 : wspec = (alpha*tn) / ((1-alpha)*fp + alpha*tn)
+    else : wspec = 0.0
+    # Return weighted specificity
+    return wspec

setup.py

@@ -7,7 +7,7 @@
 
 setup(
    name='miseval',
-   version='1.1.2',
+   version='1.1.3',
    description='A Metric Library for Medical Image Segmentation Evaluation',
    url='https://github.com/frankkramer-lab/miseval',
    author='Dominik Müller',

tests/test_mism.py

@@ -0,0 +1,58 @@
+#==============================================================================#
+#  Author:       Dominik Müller                                                #
+#  Copyright:    2022 IT-Infrastructure for Translational Medical Research,    #
+#                University of Augsburg                                        #
+#                                                                              #
+#  This program is free software: you can redistribute it and/or modify        #
+#  it under the terms of the GNU General Public License as published by        #
+#  the Free Software Foundation, either version 3 of the License, or           #
+#  (at your option) any later version.                                         #
+#                                                                              #
+#  This program is distributed in the hope that it will be useful,             #
+#  but WITHOUT ANY WARRANTY; without even the implied warranty of              #
+#  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the               #
+#  GNU General Public License for more details.                                #
+#                                                                              #
+#  You should have received a copy of the GNU General Public License           #
+#  along with this program.  If not, see <http://www.gnu.org/licenses/>.       #
+#==============================================================================#
+#-----------------------------------------------------#
+#                   Library imports                   #
+#-----------------------------------------------------#
+# External modules
+import numpy as np
+import unittest
+# Internal modules
+from miseval import *
+
+#-----------------------------------------------------#
+#                 Unittest: MISmetric                 #
+#-----------------------------------------------------#
+class TEST_MISm(unittest.TestCase):
+    @classmethod
+    def setUpClass(self):
+        # Create ground truth
+        np.random.seed(1)
+        self.gt_bi = np.random.randint(2, size=(32,32))
+        self.gt_mc = np.random.randint(5, size=(32,32))
+        # Create prediction
+        np.random.seed(2)
+        self.pd_bi = np.random.randint(2, size=(32,32))
+        self.pd_mc = np.random.randint(5, size=(32,32))
+
+    #-------------------------------------------------#
+    #              Calculate : MISmetric              #
+    #-------------------------------------------------#
+    def test_calc_MISm(self):
+        # Check binary score
+        score_bi = calc_MISm(self.gt_bi, self.pd_bi, c=1)
+        self.assertTrue(isinstance(score_bi, np.float64))
+        # Check multi-class score
+        for i in range(5):
+            score_mc = calc_MISm(self.gt_mc, self.pd_mc, c=i)
+            self.assertTrue(isinstance(score_mc, np.float64))
+        # Check existance in metric_dict
+        self.assertTrue("MISm" in metric_dict)
+        self.assertTrue(callable(metric_dict["MISm"]))
+        self.assertTrue("MISmetric" in metric_dict)
+        self.assertTrue(callable(metric_dict["MISmetric"]))

tests/test_specificity.py

@@ -74,3 +74,23 @@ def test_calc_Specificity_CM(self):
             score_mc = calc_Specificity_CM(self.gt_mc, self.pd_mc, c=i)
             self.assertTrue(isinstance(score_mc, np.float64))
             self.assertTrue(score_mc == calc_Specificity(self.gt_mc, self.pd_mc, c=i))
+
+    #-------------------------------------------------#
+    #         Calculate : Weighted Specificity        #
+    #-------------------------------------------------#
+    def test_calc_Specificity_Weighted(self):
+        # Check binary score
+        score_bi = calc_Specificity_Weighted(self.gt_bi, self.pd_bi, c=1)
+        self.assertTrue(isinstance(score_bi, np.float64))
+        self.assertTrue(score_bi == calc_Specificity_Weighted(self.gt_bi,
+                                                              self.pd_bi, c=1))
+        # Check multi-class score
+        for i in range(5):
+            score_mc = calc_Specificity_Weighted(self.gt_mc, self.pd_mc, c=i)
+            self.assertTrue(isinstance(score_mc, np.float64))
+            self.assertTrue(score_mc == calc_Specificity_Weighted(self.gt_mc,
+                                                                  self.pd_mc, c=i))
+        self.assertTrue("wSPEC" in metric_dict)
+        self.assertTrue(callable(metric_dict["wSPEC"]))
+        self.assertTrue("WeightedSpecificity" in metric_dict)
+        self.assertTrue(callable(metric_dict["WeightedSpecificity"]))