diff --git a/monai/networks/blocks/convolutions.py b/monai/networks/blocks/convolutions.py
index 8b18614364..bbd49e509a 100644
--- a/monai/networks/blocks/convolutions.py
+++ b/monai/networks/blocks/convolutions.py
@@ -231,7 +231,7 @@ class ResidualUnit(nn.Module):
             - When dropout_dim = 2, Randomly zero out entire channels (a channel is a 2D feature map).
             - When dropout_dim = 3, Randomly zero out entire channels (a channel is a 3D feature map).
 
-            The value of dropout_dim should be no larger than the value of `dimensions`.
+            The value of dropout_dim should be no larger than the value of `spatial_dims`.
         dilation: dilation rate. Defaults to 1.
         bias: whether to have a bias term. Defaults to True.
         last_conv_only: for the last subunit, whether to use the convolutional layer only.
@@ -269,7 +269,7 @@ def __init__(
         self.out_channels = out_channels
         self.conv = nn.Sequential()
         self.residual = nn.Identity()
-        if not padding:
+        if padding is None:
             padding = same_padding(kernel_size, dilation)
         schannels = in_channels
         sstrides = strides
diff --git a/tests/networks/blocks/test_convolutions.py b/tests/networks/blocks/test_convolutions.py
index f882f133e9..0bbb8fa0ab 100644
--- a/tests/networks/blocks/test_convolutions.py
+++ b/tests/networks/blocks/test_convolutions.py
@@ -151,6 +151,10 @@ def test_dropout1(self):
         expected_shape = (1, self.output_channels, self.im_shape[0], self.im_shape[1])
         self.assertEqual(out.shape, expected_shape)
 
+    def test_padding1(self):
+        conv = ResidualUnit(2, 1, self.output_channels, kernel_size=1, padding=0)
+        self.assertEqual(conv.conv.unit0.conv.padding, (0, 0))
+
 
 if __name__ == "__main__":
     unittest.main()