diff --git a/monai/networks/nets/swin_unetr.py b/monai/networks/nets/swin_unetr.py
index 9f8204968f..59fdb41815 100644
--- a/monai/networks/nets/swin_unetr.py
+++ b/monai/networks/nets/swin_unetr.py
@@ -65,6 +65,7 @@ def __init__(
         use_checkpoint: bool = False,
         spatial_dims: int = 3,
         downsample="merging",
+        use_v2=False,
     ) -> None:
         """
         Args:
@@ -84,6 +85,7 @@ def __init__(
             downsample: module used for downsampling, available options are `"mergingv2"`, `"merging"` and a
                 user-specified `nn.Module` following the API defined in :py:class:`monai.networks.nets.PatchMerging`.
                 The default is currently `"merging"` (the original version defined in v0.9.0).
+            use_v2: using swinunetr_v2, which adds a residual convolution block at the beggining of each swin stage.
 
         Examples::
 
@@ -142,6 +144,7 @@ def __init__(
             use_checkpoint=use_checkpoint,
             spatial_dims=spatial_dims,
             downsample=look_up_option(downsample, MERGING_MODE) if isinstance(downsample, str) else downsample,
+            use_v2=use_v2,
         )
 
         self.encoder1 = UnetrBasicBlock(
@@ -921,6 +924,7 @@ def __init__(
         use_checkpoint: bool = False,
         spatial_dims: int = 3,
         downsample="merging",
+        use_v2=False,
     ) -> None:
         """
         Args:
@@ -942,6 +946,7 @@ def __init__(
             downsample: module used for downsampling, available options are `"mergingv2"`, `"merging"` and a
                 user-specified `nn.Module` following the API defined in :py:class:`monai.networks.nets.PatchMerging`.
                 The default is currently `"merging"` (the original version defined in v0.9.0).
+            use_v2: using swinunetr_v2, which adds a residual convolution block at the beggining of each swin stage.
         """
 
         super().__init__()
@@ -959,10 +964,16 @@ def __init__(
         )
         self.pos_drop = nn.Dropout(p=drop_rate)
         dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]
+        self.use_v2 = use_v2
         self.layers1 = nn.ModuleList()
         self.layers2 = nn.ModuleList()
         self.layers3 = nn.ModuleList()
         self.layers4 = nn.ModuleList()
+        if self.use_v2:
+            self.layers1c = nn.ModuleList()
+            self.layers2c = nn.ModuleList()
+            self.layers3c = nn.ModuleList()
+            self.layers4c = nn.ModuleList()
         down_sample_mod = look_up_option(downsample, MERGING_MODE) if isinstance(downsample, str) else downsample
         for i_layer in range(self.num_layers):
             layer = BasicLayer(
@@ -987,6 +998,25 @@ def __init__(
                 self.layers3.append(layer)
             elif i_layer == 3:
                 self.layers4.append(layer)
+            if self.use_v2:
+                layerc = UnetrBasicBlock(
+                    spatial_dims=3,
+                    in_channels=embed_dim * 2**i_layer,
+                    out_channels=embed_dim * 2**i_layer,
+                    kernel_size=3,
+                    stride=1,
+                    norm_name="instance",
+                    res_block=True,
+                )
+                if i_layer == 0:
+                    self.layers1c.append(layerc)
+                elif i_layer == 1:
+                    self.layers2c.append(layerc)
+                elif i_layer == 2:
+                    self.layers3c.append(layerc)
+                elif i_layer == 3:
+                    self.layers4c.append(layerc)
+
         self.num_features = int(embed_dim * 2 ** (self.num_layers - 1))
 
     def proj_out(self, x, normalize=False):
@@ -1008,12 +1038,20 @@ def forward(self, x, normalize=True):
         x0 = self.patch_embed(x)
         x0 = self.pos_drop(x0)
         x0_out = self.proj_out(x0, normalize)
+        if self.use_v2:
+            x0 = self.layers1c[0](x0.contiguous())
         x1 = self.layers1[0](x0.contiguous())
         x1_out = self.proj_out(x1, normalize)
+        if self.use_v2:
+            x1 = self.layers2c[0](x1.contiguous())
         x2 = self.layers2[0](x1.contiguous())
         x2_out = self.proj_out(x2, normalize)
+        if self.use_v2:
+            x2 = self.layers3c[0](x2.contiguous())
         x3 = self.layers3[0](x2.contiguous())
         x3_out = self.proj_out(x3, normalize)
+        if self.use_v2:
+            x3 = self.layers4c[0](x3.contiguous())
         x4 = self.layers4[0](x3.contiguous())
         x4_out = self.proj_out(x4, normalize)
         return [x0_out, x1_out, x2_out, x3_out, x4_out]