diff --git a/docs/source/networks.rst b/docs/source/networks.rst
index 62a1c82f2f..39889266d9 100644
--- a/docs/source/networks.rst
+++ b/docs/source/networks.rst
@@ -538,6 +538,11 @@ Nets
 .. autoclass:: TorchVisionFullyConvModel
   :members:
 
+`MILModel`
+~~~~~~~~~~
+.. autoclass:: MILModel
+  :members:
+
 Utilities
 ---------
 .. automodule:: monai.networks.utils
diff --git a/monai/networks/nets/milmodel.py b/monai/networks/nets/milmodel.py
index 213a86864f..c7fb7f3937 100644
--- a/monai/networks/nets/milmodel.py
+++ b/monai/networks/nets/milmodel.py
@@ -13,23 +13,22 @@ class MILModel(nn.Module):
     Multiple Instance Learning (MIL) model, with a backbone classification model.
 
     Args:
-        num_classes: number of output classes
-        mil_mode: MIL algorithm (either mean, max, att, att_trans, att_trans_pyramid)
+        num_classes: number of output classes.
+        mil_mode: MIL algorithm, available values:
+            "mean" - average features from all instances, equivalent to pure CNN (non MIL).
+            "max - retain only the instance with the max probability for loss calculation.
+            "att" - attention based MIL https://arxiv.org/abs/1802.04712.
+            "att_trans" - transformer MIL https://arxiv.org/abs/2111.01556.
+            "att_trans_pyramid" - transformer pyramid MIL https://arxiv.org/abs/2111.01556.
             Defaults to ``att``.
-        pretrained: init backbone with pretrained weights.
-            Defaults to ``True``.
-        backbone: Backbone classifier CNN. (either None, nn.Module that returns features,
-                  or a string name of a torchvision model)
+        pretrained: init backbone with pretrained weights, defaults to ``True``.
+        backbone: Backbone classifier CNN (either None, nn.Module that returns features,
+            or a string name of a torchvision model).
             Defaults to ``None``, in which case ResNet50 is used.
         backbone_num_features: Number of output features of the backbone CNN
             Defaults to ``None`` (necessary only when using a custom backbone)
-
-    mil_mode:
-        "mean" - average features from all instances, equivalent to pure CNN (non MIL)
-        "max - retain only the instance with the max probability for loss calculation
-        "att" - attention based MIL https://arxiv.org/abs/1802.04712
-        "att_trans" - transformer MIL https://arxiv.org/abs/2111.01556
-        "att_trans_pyramid" - transformer pyramid MIL https://arxiv.org/abs/2111.01556
+        trans_blocks: number of the blocks in `TransformEncoder` layer.
+        trans_dropout: dropout rate in `TransformEncoder` layer.
 
     """
 
@@ -53,7 +52,6 @@ def __init__(
             raise ValueError("Unsupported mil_mode: " + str(mil_mode))
 
         self.mil_mode = mil_mode.lower()
-        print("MILModel with mode", mil_mode, "num_classes", num_classes)
         self.attention = nn.Sequential()
         self.transformer = None  # type: Optional[nn.Module]