diff --git a/generative/losses/perceptual.py b/generative/losses/perceptual.py
index 30314467..932ae613 100644
--- a/generative/losses/perceptual.py
+++ b/generative/losses/perceptual.py
@@ -21,8 +21,9 @@ class PerceptualLoss(nn.Module):
     Perceptual loss using features from pretrained deep neural networks trained. The function supports networks
     pretrained on: ImageNet that use the LPIPS approach from Zhang, et al. "The unreasonable effectiveness of deep
     features as a perceptual metric." https://arxiv.org/abs/1801.03924 ; RadImagenet from Mei, et al. "RadImageNet: An
-    Open Radiologic Deep Learning Research Dataset for Effective Transfer Learning"; and MedicalNet from Chen et al.
-    "Med3D: Transfer Learning for 3D Medical Image Analysis" .
+    Open Radiologic Deep Learning Research Dataset for Effective Transfer Learning"
+    https://pubs.rsna.org/doi/full/10.1148/ryai.210315 ; and MedicalNet from Chen et al. "Med3D: Transfer Learning for
+    3D Medical Image Analysis" https://arxiv.org/abs/1904.00625 .
 
     The fake 3D implementation is based on a 2.5D approach where we calculate the 2D perceptual on slices from the
     three axis.
@@ -48,11 +49,14 @@ def __init__(
         if spatial_dims not in [2, 3]:
             raise NotImplementedError("Perceptual loss is implemented only in 2D and 3D.")
 
+        if spatial_dims == 2 and "medicalnet_" in network_type:
+            raise ValueError("MedicalNet networks are only compatible with ``spatial_dims=3``.")
+
         self.spatial_dims = spatial_dims
         if spatial_dims == 3 and is_fake_3d is False:
-            self.perceptual_function = MedicalNetPerceptualComponent(net=network_type, verbose=False)
+            self.perceptual_function = MedicalNetPerceptualSimilarity(net=network_type, verbose=False)
         elif "radimagenet_" in network_type:
-            self.perceptual_function = RadImageNetPerceptualComponent(net=network_type, verbose=False)
+            self.perceptual_function = RadImageNetPerceptualSimilarity(net=network_type, verbose=False)
         else:
             self.perceptual_function = LPIPS(
                 pretrained=True,
@@ -134,7 +138,7 @@ def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
         return torch.mean(loss)
 
 
-class MedicalNetPerceptualComponent(nn.Module):
+class MedicalNetPerceptualSimilarity(nn.Module):
     """
     Component to perform the perceptual evaluation with the networks pretrained by Chen, et al. "Med3D: Transfer
     Learning for 3D Medical Image Analysis". This class uses torch Hub to download the networks from
@@ -200,7 +204,7 @@ def medicalnet_intensity_normalisation(volume):
     return (volume - mean) / std
 
 
-class RadImageNetPerceptualComponent(nn.Module):
+class RadImageNetPerceptualSimilarity(nn.Module):
     """
     Component to perform the perceptual evaluation with the networks pretrained on RadImagenet (pretrained by Mei, et
     al. "RadImageNet: An Open Radiologic Deep Learning Research Dataset for Effective Transfer Learning"). This class
diff --git a/tests/test_perceptual_loss.py b/tests/test_perceptual_loss.py
index bd1521ea..ff20758e 100644
--- a/tests/test_perceptual_loss.py
+++ b/tests/test_perceptual_loss.py
@@ -76,6 +76,13 @@ def test_1d(self):
         with self.assertRaises(NotImplementedError):
             PerceptualLoss(spatial_dims=1)
 
+    def test_medicalnet_on_2d_data(self):
+        with self.assertRaises(ValueError):
+            PerceptualLoss(spatial_dims=2, network_type="medicalnet_resnet10_23datasets")
+
+        with self.assertRaises(ValueError):
+            PerceptualLoss(spatial_dims=2, network_type="medicalnet_resnet50_23datasets")
+
 
 if __name__ == "__main__":
     unittest.main()