Repository: Project-MONAI/MONAI

Issue:
Add DeleteChannelD inverse equivalent of RepeatChannelD
In the liver and tumor segmentation model, it's possible to approximate the Brain Segmentation workflow with RepeatChannelD to enable usage of ConvertToMultiChannelBasedOnBratsClassesd. However this results in greater, unnecessary memory usage.

Adding a DeleteChannelD transform would allow the duplicated channels to be pared down before feeding into the network.


Discussion:

Resolving patch:
diff --git a/monai/transforms/__init__.py b/monai/transforms/__init__.py
--- a/monai/transforms/__init__.py
+++ b/monai/transforms/__init__.py
@@ -250,6 +250,7 @@
     Identity,
     LabelToMask,
     Lambda,
+    RemoveRepeatedChannel,
     RepeatChannel,
     SimulateDelay,
     SplitChannel,
@@ -305,6 +306,9 @@
     RandLambdad,
     RandLambdaD,
     RandLambdaDict,
+    RemoveRepeatedChanneld,
+    RemoveRepeatedChannelD,
+    RemoveRepeatedChannelDict,
     RepeatChanneld,
     RepeatChannelD,
     RepeatChannelDict,
diff --git a/monai/transforms/utility/array.py b/monai/transforms/utility/array.py
--- a/monai/transforms/utility/array.py
+++ b/monai/transforms/utility/array.py
@@ -31,6 +31,7 @@
     "AsChannelLast",
     "AddChannel",
     "RepeatChannel",
+    "RemoveRepeatedChannel",
     "SplitChannel",
     "CastToType",
     "ToTensor",
@@ -161,6 +162,32 @@ def __call__(self, img: np.ndarray) -> np.ndarray:
         return np.repeat(img, self.repeats, 0)
 
 
+class RemoveRepeatedChannel(Transform):
+    """
+    RemoveRepeatedChannel data to undo RepeatChannel
+    The `repeats` count specifies the deletion of the origin data, for example:
+    ``RemoveRepeatedChannel(repeats=2)([[1, 2], [1, 2], [3, 4], [3, 4]])`` generates: ``[[1, 2], [3, 4]]``
+
+    Args:
+        repeats: the number of repetitions to be deleted for each element.
+    """
+
+    def __init__(self, repeats: int) -> None:
+        if repeats <= 0:
+            raise AssertionError("repeats count must be greater than 0.")
+
+        self.repeats = repeats
+
+    def __call__(self, img: np.ndarray) -> np.ndarray:
+        """
+        Apply the transform to `img`, assuming `img` is a "channel-first" array.
+        """
+        if np.shape(img)[0] < 2:
+            raise AssertionError("Image must have more than one channel")
+
+        return np.array(img[:: self.repeats, :])
+
+
 class SplitChannel(Transform):
     """
     Split Numpy array or PyTorch Tensor data according to the channel dim.
diff --git a/monai/transforms/utility/dictionary.py b/monai/transforms/utility/dictionary.py
--- a/monai/transforms/utility/dictionary.py
+++ b/monai/transforms/utility/dictionary.py
@@ -35,6 +35,7 @@
     Identity,
     LabelToMask,
     Lambda,
+    RemoveRepeatedChannel,
     RepeatChannel,
     SimulateDelay,
     SplitChannel,
@@ -52,6 +53,7 @@
     "AsChannelLastd",
     "AddChanneld",
     "RepeatChanneld",
+    "RemoveRepeatedChanneld",
     "SplitChanneld",
     "CastToTyped",
     "ToTensord",
@@ -82,6 +84,8 @@
     "RandLambdaDict",
     "RepeatChannelD",
     "RepeatChannelDict",
+    "RemoveRepeatedChannelD",
+    "RemoveRepeatedChannelDict",
     "SplitChannelD",
     "SplitChannelDict",
     "CastToTypeD",
@@ -226,6 +230,28 @@ def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.nda
         return d
 
 
+class RemoveRepeatedChanneld(MapTransform):
+    """
+    Dictionary-based wrapper of :py:class:`monai.transforms.RemoveRepeatedChannel`.
+    """
+
+    def __init__(self, keys: KeysCollection, repeats: int) -> None:
+        """
+        Args:
+            keys: keys of the corresponding items to be transformed.
+                See also: :py:class:`monai.transforms.compose.MapTransform`
+            repeats: the number of repetitions for each element.
+        """
+        super().__init__(keys)
+        self.repeater = RemoveRepeatedChannel(repeats)
+
+    def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]:
+        d = dict(data)
+        for key in self.keys:
+            d[key] = self.repeater(d[key])
+        return d
+
+
 class SplitChanneld(MapTransform):
     """
     Dictionary-based wrapper of :py:class:`monai.transforms.SplitChannel`.
@@ -836,6 +862,7 @@ def __call__(self, data: Mapping[Hashable, torch.Tensor]) -> Dict[Hashable, torc
 AsChannelFirstD = AsChannelFirstDict = AsChannelFirstd
 AsChannelLastD = AsChannelLastDict = AsChannelLastd
 AddChannelD = AddChannelDict = AddChanneld
+RemoveRepeatedChannelD = RemoveRepeatedChannelDict = RemoveRepeatedChanneld
 RepeatChannelD = RepeatChannelDict = RepeatChanneld
 SplitChannelD = SplitChannelDict = SplitChanneld
 CastToTypeD = CastToTypeDict = CastToTyped


Test patch:
diff --git a/tests/test_remove_repeated_channel.py b/tests/test_remove_repeated_channel.py
new file mode 100644
--- /dev/null
+++ b/tests/test_remove_repeated_channel.py
@@ -0,0 +1,30 @@
+# Copyright 2020 - 2021 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 numpy as np
+from parameterized import parameterized
+
+from monai.transforms import RemoveRepeatedChannel
+
+TEST_CASE_1 = [{"repeats": 2}, np.array([[1, 2], [1, 2], [3, 4], [3, 4]]), (2, 2)]
+
+
+class TestRemoveRepeatedChannel(unittest.TestCase):
+    @parameterized.expand([TEST_CASE_1])
+    def test_shape(self, input_param, input_data, expected_shape):
+        result = RemoveRepeatedChannel(**input_param)(input_data)
+        self.assertEqual(result.shape, expected_shape)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/test_remove_repeated_channeld.py b/tests/test_remove_repeated_channeld.py
new file mode 100644
--- /dev/null
+++ b/tests/test_remove_repeated_channeld.py
@@ -0,0 +1,34 @@
+# Copyright 2020 - 2021 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 numpy as np
+from parameterized import parameterized
+
+from monai.transforms import RemoveRepeatedChanneld
+
+TEST_CASE_1 = [
+    {"keys": ["img"], "repeats": 2},
+    {"img": np.array([[1, 2], [1, 2], [3, 4], [3, 4]]), "seg": np.array([[1, 2], [1, 2], [3, 4], [3, 4]])},
+    (2, 2),
+]
+
+
+class TestRemoveRepeatedChanneld(unittest.TestCase):
+    @parameterized.expand([TEST_CASE_1])
+    def test_shape(self, input_param, input_data, expected_shape):
+        result = RemoveRepeatedChanneld(**input_param)(input_data)
+        self.assertEqual(result["img"].shape, expected_shape)
+
+
+if __name__ == "__main__":
+    unittest.main()