diff --git a/monai/apps/pathology/transforms/post/array.py b/monai/apps/pathology/transforms/post/array.py
index 5289dc101c..248ff24bec 100644
--- a/monai/apps/pathology/transforms/post/array.py
+++ b/monai/apps/pathology/transforms/post/array.py
@@ -31,7 +31,7 @@
 from monai.transforms.utils_pytorch_numpy_unification import max, maximum, min, sum, unique
 from monai.utils import TransformBackends, convert_to_numpy, optional_import
 from monai.utils.misc import ensure_tuple_rep
-from monai.utils.type_conversion import convert_to_dst_type
+from monai.utils.type_conversion import convert_to_dst_type, convert_to_tensor
 
 label, _ = optional_import("scipy.ndimage.measurements", name="label")
 disk, _ = optional_import("skimage.morphology", name="disk")
@@ -671,6 +671,7 @@ class HoVerNetInstanceMapPostProcessing(Transform):
         min_num_points: minimum number of points to be considered as a contour. Defaults to 3.
         contour_level: an optional value for `skimage.measure.find_contours` to find contours in the array.
             If not provided, the level is set to `(max(image) + min(image)) / 2`.
+        device: target device to put the output Tensor data.
     """
 
     def __init__(
@@ -686,9 +687,10 @@ def __init__(
         watershed_connectivity: int | None = 1,
         min_num_points: int = 3,
         contour_level: float | None = None,
+        device: str | torch.device | None = None,
     ) -> None:
         super().__init__()
-
+        self.device = device
         self.generate_watershed_mask = GenerateWatershedMask(
             activation=activation, threshold=mask_threshold, min_object_size=min_object_size
         )
@@ -742,7 +744,7 @@ def __call__(  # type: ignore
                     "centroid": instance_centroid,
                     "contour": instance_contour,
                 }
-
+        instance_map = convert_to_tensor(instance_map, device=self.device)
         return instance_info, instance_map
 
 
@@ -758,13 +760,19 @@ class HoVerNetNuclearTypePostProcessing(Transform):
         threshold: an optional float value to threshold to binarize probability map.
             If not provided, defaults to 0.5 when activation is not "softmax", otherwise None.
         return_type_map: whether to calculate and return pixel-level type map.
+        device: target device to put the output Tensor data.
 
     """
 
     def __init__(
-        self, activation: str | Callable = "softmax", threshold: float | None = None, return_type_map: bool = True
+        self,
+        activation: str | Callable = "softmax",
+        threshold: float | None = None,
+        return_type_map: bool = True,
+        device: str | torch.device | None = None,
     ) -> None:
         super().__init__()
+        self.device = device
         self.return_type_map = return_type_map
         self.generate_instance_type = GenerateInstanceType()
 
@@ -824,5 +832,6 @@ def __call__(  # type: ignore
             # update instance type map
             if type_map is not None:
                 type_map[instance_map == inst_id] = instance_type
+                type_map = convert_to_tensor(type_map, device=self.device)
 
         return instance_info, type_map
diff --git a/monai/apps/pathology/transforms/post/dictionary.py b/monai/apps/pathology/transforms/post/dictionary.py
index ef6de1b596..a95bdfd48f 100644
--- a/monai/apps/pathology/transforms/post/dictionary.py
+++ b/monai/apps/pathology/transforms/post/dictionary.py
@@ -14,6 +14,7 @@
 from collections.abc import Callable, Hashable, Mapping
 
 import numpy as np
+import torch
 
 from monai.apps.pathology.transforms.post.array import (
     GenerateDistanceMap,
@@ -488,6 +489,7 @@ class HoVerNetInstanceMapPostProcessingd(Transform):
         min_num_points: minimum number of points to be considered as a contour. Defaults to 3.
         contour_level: an optional value for `skimage.measure.find_contours` to find contours in the array.
             If not provided, the level is set to `(max(image) + min(image)) / 2`.
+        device: target device to put the output Tensor data.
     """
 
     def __init__(
@@ -507,6 +509,7 @@ def __init__(
         watershed_connectivity: int | None = 1,
         min_num_points: int = 3,
         contour_level: float | None = None,
+        device: str | torch.device | None = None,
     ) -> None:
         super().__init__()
         self.instance_map_post_process = HoVerNetInstanceMapPostProcessing(
@@ -521,6 +524,7 @@ def __init__(
             watershed_connectivity=watershed_connectivity,
             min_num_points=min_num_points,
             contour_level=contour_level,
+            device=device,
         )
         self.nuclear_prediction_key = nuclear_prediction_key
         self.hover_map_key = hover_map_key
@@ -553,7 +557,7 @@ class HoVerNetNuclearTypePostProcessingd(Transform):
             Defaults to `"instance_info"`.
         instance_map_key: the key where instance map is stored. Defaults to `"instance_map"`.
         type_map_key: the output key where type map is written. Defaults to `"type_map"`.
-
+        device: target device to put the output Tensor data.
 
     """
 
@@ -566,10 +570,11 @@ def __init__(
         activation: str | Callable = "softmax",
         threshold: float | None = None,
         return_type_map: bool = True,
+        device: str | torch.device | None = None,
     ) -> None:
         super().__init__()
         self.type_post_process = HoVerNetNuclearTypePostProcessing(
-            activation=activation, threshold=threshold, return_type_map=return_type_map
+            activation=activation, threshold=threshold, return_type_map=return_type_map, device=device
         )
         self.type_prediction_key = type_prediction_key
         self.instance_info_key = instance_info_key