diff --git a/monai/metrics/froc.py b/monai/metrics/froc.py
index 81a890aa68..3faef84917 100644
--- a/monai/metrics/froc.py
+++ b/monai/metrics/froc.py
@@ -11,7 +11,7 @@
 
 from __future__ import annotations
 
-from typing import Any, cast
+from typing import Any
 
 import numpy as np
 import torch
@@ -67,12 +67,14 @@ def compute_fp_tp_probs_nd(
 
     hittedlabel = evaluation_mask[tuple(coords.T)]
     fp_probs = probs[np.where(hittedlabel == 0)]
+    num_targets = 0
     for i in range(1, max_label + 1):
-        if i not in labels_to_exclude and i in hittedlabel:
-            tp_probs[i - 1] = probs[np.where(hittedlabel == i)].max()
+        if i not in labels_to_exclude:
+            num_targets += 1
+            if i in hittedlabel:
+                tp_probs[i - 1] = probs[np.where(hittedlabel == i)].max()
 
-    num_targets = max_label - len(labels_to_exclude)
-    return fp_probs, tp_probs, cast(int, num_targets)
+    return fp_probs, tp_probs, num_targets
 
 
 def compute_fp_tp_probs(
diff --git a/tests/metrics/test_compute_froc.py b/tests/metrics/test_compute_froc.py
index 4dc0507366..aa889ddb07 100644
--- a/tests/metrics/test_compute_froc.py
+++ b/tests/metrics/test_compute_froc.py
@@ -60,6 +60,34 @@
     3,
 ]
 
+TEST_CASE_EXCLUDE_ABSENT = [
+    {
+        "probs": torch.tensor([1, 0.6, 0.8]),
+        "y_coord": torch.tensor([0, 2, 3]),
+        "x_coord": torch.tensor([3, 0, 1]),
+        "evaluation_mask": np.array([[0, 0, 1, 1], [2, 2, 0, 0], [0, 3, 3, 0], [0, 3, 3, 3]]),
+        "labels_to_exclude": [5],
+        "resolution_level": 0,
+    },
+    np.array([0.6]),
+    np.array([1, 0, 0.8]),
+    3,
+]
+
+TEST_CASE_EXCLUDE_DUPLICATE = [
+    {
+        "probs": torch.tensor([1, 0.6, 0.8]),
+        "y_coord": torch.tensor([0, 2, 3]),
+        "x_coord": torch.tensor([3, 0, 1]),
+        "evaluation_mask": np.array([[0, 0, 1, 1], [2, 2, 0, 0], [0, 3, 3, 0], [0, 3, 3, 3]]),
+        "labels_to_exclude": [2, 2],
+        "resolution_level": 0,
+    },
+    np.array([0.6]),
+    np.array([1, 0, 0.8]),
+    2,
+]
+
 TEST_CASE_4 = [
     {
         "fp_probs": np.array([0.8, 0.6]),
@@ -112,7 +140,9 @@
 
 class TestComputeFpTp(unittest.TestCase):
 
-    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3])
+    @parameterized.expand(
+        [TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_EXCLUDE_ABSENT, TEST_CASE_EXCLUDE_DUPLICATE]
+    )
     def test_value(self, input_data, expected_fp, expected_tp, expected_num):
         fp_probs, tp_probs, num_tumors = compute_fp_tp_probs(**input_data)
         np.testing.assert_allclose(fp_probs, expected_fp, rtol=1e-5)