diff --git a/monai/apps/pathology/engines/__init__.py b/monai/apps/pathology/engines/__init__.py
new file mode 100644
index 0000000000..68c084d40d
--- /dev/null
+++ b/monai/apps/pathology/engines/__init__.py
@@ -0,0 +1,12 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from .utils import PrepareBatchHoVerNet
diff --git a/monai/apps/pathology/engines/utils.py b/monai/apps/pathology/engines/utils.py
new file mode 100644
index 0000000000..3a190a146b
--- /dev/null
+++ b/monai/apps/pathology/engines/utils.py
@@ -0,0 +1,54 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Dict, Optional, Sequence, Union
+
+import torch
+
+from monai.engines import PrepareBatch, PrepareBatchExtraInput
+from monai.utils import ensure_tuple
+from monai.utils.enums import HoVerNetBranch
+
+__all__ = ["PrepareBatchHoVerNet"]
+
+
+class PrepareBatchHoVerNet(PrepareBatch):
+    """
+    Customized prepare batch callable for trainers or evaluators which support label to be a dictionary.
+    Extra items are specified by the `extra_keys` parameter and are extracted from the input dictionary (ie. the batch).
+    This assumes label is a dictionary.
+
+    Args:
+        extra_keys: If a sequence of strings is provided, values from the input dictionary are extracted from
+            those keys and passed to the nework as extra positional arguments.
+    """
+
+    def __init__(self, extra_keys: Sequence[str]) -> None:
+        if len(ensure_tuple(extra_keys)) != 2:
+            raise ValueError(f"length of `extra_keys` should be 2, get {len(ensure_tuple(extra_keys))}")
+        self.prepare_batch = PrepareBatchExtraInput(extra_keys)
+
+    def __call__(
+        self,
+        batchdata: Dict[str, torch.Tensor],
+        device: Optional[Union[str, torch.device]] = None,
+        non_blocking: bool = False,
+        **kwargs,
+    ):
+        """
+        Args `batchdata`, `device`, `non_blocking` refer to the ignite API:
+        https://pytorch.org/ignite/v0.4.8/generated/ignite.engine.create_supervised_trainer.html.
+        `kwargs` supports other args for `Tensor.to()` API.
+        """
+        image, _label, extra_label, _ = self.prepare_batch(batchdata, device, non_blocking, **kwargs)
+        label = {HoVerNetBranch.NP: _label, HoVerNetBranch.NC: extra_label[0], HoVerNetBranch.HV: extra_label[1]}
+
+        return image, label
diff --git a/tests/min_tests.py b/tests/min_tests.py
index 915e67e120..765cec8adf 100644
--- a/tests/min_tests.py
+++ b/tests/min_tests.py
@@ -140,6 +140,7 @@ def run_testsuit():
         "test_png_saver",
         "test_prepare_batch_default",
         "test_prepare_batch_extra_input",
+        "test_prepare_batch_hovernet",
         "test_rand_grid_patch",
         "test_rand_rotate",
         "test_rand_rotated",
diff --git a/tests/test_prepare_batch_hovernet.py b/tests/test_prepare_batch_hovernet.py
new file mode 100644
index 0000000000..9aed8e94c7
--- /dev/null
+++ b/tests/test_prepare_batch_hovernet.py
@@ -0,0 +1,66 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+import torch
+from parameterized import parameterized
+
+from monai.apps.pathology.engines import PrepareBatchHoVerNet
+from monai.engines import SupervisedEvaluator
+from monai.utils.enums import HoVerNetBranch
+from tests.utils import assert_allclose
+
+TEST_CASE_0 = [
+    {"extra_keys": ["extra_label1", "extra_label2"]},
+    {HoVerNetBranch.NP: torch.tensor([1, 2]), HoVerNetBranch.NC: torch.tensor([4, 4]), HoVerNetBranch.HV: 16},
+]
+
+
+class TestNet(torch.nn.Module):
+    def forward(self, x: torch.Tensor):
+        return {HoVerNetBranch.NP: torch.tensor([1, 2]), HoVerNetBranch.NC: torch.tensor([4, 4]), HoVerNetBranch.HV: 16}
+
+
+class TestPrepareBatchHoVerNet(unittest.TestCase):
+    @parameterized.expand([TEST_CASE_0])
+    def test_content(self, input_args, expected_value):
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        dataloader = [
+            {
+                "image": torch.tensor([1, 2]),
+                "label": torch.tensor([1, 2]),
+                "extra_label1": torch.tensor([3, 4]),
+                "extra_label2": 16,
+            }
+        ]
+        # set up engine
+        evaluator = SupervisedEvaluator(
+            device=device,
+            val_data_loader=dataloader,
+            epoch_length=1,
+            network=TestNet(),
+            non_blocking=True,
+            prepare_batch=PrepareBatchHoVerNet(**input_args),
+            decollate=False,
+        )
+        evaluator.run()
+        output = evaluator.state.output
+        assert_allclose(output["image"], torch.tensor([1, 2], device=device))
+        for k, v in output["pred"].items():
+            if isinstance(v, torch.Tensor):
+                assert_allclose(v, expected_value[k].to(device))
+            else:
+                self.assertEqual(v, expected_value[k])
+
+
+if __name__ == "__main__":
+    unittest.main()