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fix(losses): bin each sample from its own range in b-spline GlobalMutualInformationLoss #9186

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fix(losses): bin each sample from its own range in b-spline GlobalMutualInformationLoss #9186

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6 changes: 5 additions & 1 deletion monai/losses/image_dissimilarity.py
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Original file line number Diff line number Diff line change
Expand Up @@ -295,7 +295,11 @@ def parzen_windowing_b_spline(self, img: torch.Tensor, order: int) -> tuple[torc
compute_img = (
img.float() if not torch.is_floating_point(img) or img.dtype in (torch.float16, torch.bfloat16) else img
)
_max, _min = torch.max(compute_img), torch.min(compute_img)
# Bin each image from its own intensity range (as in ITK's Mattes MI): a batch-wide min/max would make one
# sample's loss depend on the other images in the batch.
reduce_dims = tuple(range(1, compute_img.ndim))
_max = torch.amax(compute_img, dim=reduce_dims, keepdim=True)
_min = torch.amin(compute_img, dim=reduce_dims, keepdim=True)
padding = 2
interior_bins = self.num_bins - 2 * padding
value_range = _max - _min
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17 changes: 17 additions & 0 deletions tests/losses/image_dissimilarity/test_global_mutual_information_loss.py
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Expand Up @@ -271,6 +271,23 @@ def test_b_spline_nonzero_ranges_are_finite(self, case_name, dtype, maximum):
self.assertIsNotNone(pred.grad)
self.assertTrue(torch.isfinite(pred.grad).all())

def test_b_spline_batch_samples_are_independent(self):
"""Verify each sample is binned from its own intensity range, not the batch's.

A per-sample loss must not change when other images with a different intensity range share the batch.
"""
generator = torch.Generator().manual_seed(0)
pred_a = torch.rand(1, 1, 32, 32, generator=generator, dtype=torch.float64)
target_a = 0.8 * pred_a + 0.2 * torch.rand(1, 1, 32, 32, generator=generator, dtype=torch.float64)
pred_b = 10 * torch.rand(1, 1, 32, 32, generator=generator, dtype=torch.float64)
target_b = 0.8 * pred_b + 2 * torch.rand(1, 1, 32, 32, generator=generator, dtype=torch.float64)
loss = GlobalMutualInformationLoss(kernel_type="b-spline", reduction="none")

alone = torch.cat([loss(pred_a, target_a).flatten(), loss(pred_b, target_b).flatten()])
batched = loss(torch.cat([pred_a, pred_b]), torch.cat([target_a, target_b])).flatten()

np.testing.assert_allclose(batched.numpy(), alone.numpy(), rtol=1e-6)


class TestGlobalMutualInformationLossBuffers(unittest.TestCase):
def test_gaussian_kernel_registers_buffers(self):
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