diff --git a/monai/networks/nets/hovernet.py b/monai/networks/nets/hovernet.py
index 323e107fd7..3ec1cea37e 100644
--- a/monai/networks/nets/hovernet.py
+++ b/monai/networks/nets/hovernet.py
@@ -443,6 +443,8 @@ class HoVerNet(nn.Module):
         adapt_standard_resnet: if the pretrained weights of the encoder follow the original format (preact-resnet50), this
             value should be `False`. If using the pretrained weights that follow torchvision's standard resnet50 format,
             this value should be `True`.
+        pretrained_state_dict_key: this arg is used when `pretrained_url` is provided and `adapt_standard_resnet` is True.
+            It is used to extract the expected state dict.
         freeze_encoder: whether to freeze the encoder of the network.
     """
 
@@ -461,6 +463,7 @@ def __init__(
         dropout_prob: float = 0.0,
         pretrained_url: str | None = None,
         adapt_standard_resnet: bool = False,
+        pretrained_state_dict_key: str | None = None,
         freeze_encoder: bool = False,
     ) -> None:
         super().__init__()
@@ -566,7 +569,7 @@ def __init__(
 
         if pretrained_url is not None:
             if adapt_standard_resnet:
-                weights = _remap_standard_resnet_model(pretrained_url)
+                weights = _remap_standard_resnet_model(pretrained_url, state_dict_key=pretrained_state_dict_key)
             else:
                 weights = _remap_preact_resnet_model(pretrained_url)
             _load_pretrained_encoder(self, weights)
@@ -609,6 +612,12 @@ def _load_pretrained_encoder(model: nn.Module, state_dict: OrderedDict | dict):
 
     model_dict.update(state_dict)
     model.load_state_dict(model_dict)
+    if len(state_dict.keys()) == 0:
+        warnings.warn(
+            "no key will be updated. Please check if 'pretrained_url' or `pretrained_state_dict_key` is correct."
+        )
+    else:
+        print(f"{len(state_dict)} out of {len(model_dict)} keys are updated with pretrained weights.")
 
 
 def _remap_preact_resnet_model(model_url: str):
@@ -619,7 +628,9 @@ def _remap_preact_resnet_model(model_url: str):
     # download the pretrained weights into torch hub's default dir
     weights_dir = os.path.join(torch.hub.get_dir(), "preact-resnet50.pth")
     download_url(model_url, fuzzy=True, filepath=weights_dir, progress=False)
-    state_dict = torch.load(weights_dir, map_location=None)["desc"]
+    state_dict = torch.load(weights_dir, map_location=None if torch.cuda.is_available() else torch.device("cpu"))[
+        "desc"
+    ]
     for key in list(state_dict.keys()):
         new_key = None
         if pattern_conv0.match(key):
@@ -639,7 +650,7 @@ def _remap_preact_resnet_model(model_url: str):
     return state_dict
 
 
-def _remap_standard_resnet_model(model_url: str):
+def _remap_standard_resnet_model(model_url: str, state_dict_key: str | None = None):
     pattern_conv0 = re.compile(r"^conv1\.(.+)$")
     pattern_bn1 = re.compile(r"^bn1\.(.+)$")
     pattern_block = re.compile(r"^layer(\d+)\.(\d+)\.(.+)$")
@@ -652,7 +663,9 @@ def _remap_standard_resnet_model(model_url: str):
     # download the pretrained weights into torch hub's default dir
     weights_dir = os.path.join(torch.hub.get_dir(), "resnet50.pth")
     download_url(model_url, fuzzy=True, filepath=weights_dir, progress=False)
-    state_dict = torch.load(weights_dir, map_location=None)
+    state_dict = torch.load(weights_dir, map_location=None if torch.cuda.is_available() else torch.device("cpu"))
+    if state_dict_key is not None:
+        state_dict = state_dict[state_dict_key]
 
     for key in list(state_dict.keys()):
         new_key = None