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Fix FROC num_targets miscount when labels_to_exclude is set #8951

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Fix FROC num_targets miscount when labels_to_exclude is set #8951

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5b0f0cf
Fix FROC num_targets miscount when labels_to_exclude is set
aymuos15 Jun 25, 2026
679e534
Add regression tests for FROC num_targets with excluded labels
aymuos15 Jun 25, 2026
7b8789e
Merge branch 'dev' into fix/froc-num-targets-exclude
aymuos15 Jun 25, 2026
b0b76c9
Remove redundant cast to int for num_targets
aymuos15 Jun 25, 2026
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12 changes: 7 additions & 5 deletions monai/metrics/froc.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,7 +11,7 @@

from __future__ import annotations

from typing import Any, cast
from typing import Any

import numpy as np
import torch
Expand Down Expand Up @@ -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(
Expand Down
32 changes: 31 additions & 1 deletion tests/metrics/test_compute_froc.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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]),
Expand Down Expand Up @@ -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)
Expand Down
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