diff --git a/didactic/config/experiment/cardinal/multimodal-xformer.yaml b/didactic/config/experiment/cardinal/multimodal-xformer.yaml
index 0dfde958..a14a93ec 100644
--- a/didactic/config/experiment/cardinal/multimodal-xformer.yaml
+++ b/didactic/config/experiment/cardinal/multimodal-xformer.yaml
@@ -3,6 +3,7 @@
 defaults:
   - /task/img_tokenizer/model: linear-embedding
   - /task/model/encoder: ???
+  - /task/model/contrastive_head: mlp
   - override /task/model: null  # Set this to null because we specify multiple submodels instead of a singleton model
   - override /task/optim: adamw
   - override /data: cardinal
diff --git a/didactic/config/task/model/contrastive_head/mlp.yaml b/didactic/config/task/model/contrastive_head/mlp.yaml
new file mode 100644
index 00000000..3e4dbe05
--- /dev/null
+++ b/didactic/config/task/model/contrastive_head/mlp.yaml
@@ -0,0 +1,7 @@
+# Build the projection head as an MLP with a single hidden layer and constant width, as proposed in
+# https://arxiv.org/abs/2106.15147
+_target_: vital.models.classification.mlp.MLP
+input_shape: [task.embed_dim]
+output_shape: [task.embed_dim]
+hidden: [task.embed_dim]
+dropout: 0
diff --git a/didactic/tasks/cardiac_multimodal_representation.py b/didactic/tasks/cardiac_multimodal_representation.py
index 2ef3b810..58229a54 100644
--- a/didactic/tasks/cardiac_multimodal_representation.py
+++ b/didactic/tasks/cardiac_multimodal_representation.py
@@ -17,7 +17,6 @@
 from vital.data.cardinal.config import View as ViewEnum
 from vital.data.cardinal.datapipes import MISSING_CAT_ATTR, PatientData, filter_image_attributes
 from vital.data.cardinal.utils.attributes import CLINICAL_CAT_ATTR_LABELS
-from vital.models.classification.mlp import MLP
 from vital.tasks.generic import SharedStepsTask
 from vital.utils.decorators import auto_move_data
 
@@ -293,13 +292,10 @@ def configure_model(
         # Build the transformer encoder
         encoder = hydra.utils.instantiate(self.hparams.model.encoder)
 
-        # Build the projection head as an MLP with a single hidden layer and constant width, as proposed in
-        # https://arxiv.org/abs/2106.15147
+        # Build the projection head for contrastive learning, if contrastive learning is enabled
         contrastive_head = None
         if self.contrastive_loss:
-            contrastive_head = MLP(
-                (self.hparams.embed_dim,), (self.hparams.embed_dim,), hidden=(self.hparams.embed_dim,), dropout=0
-            )
+            contrastive_head = hydra.utils.instantiate(self.hparams.model.contrastive_head)
 
         # Build the prediction heads (one by clinical attribute to predict) following the architecture proposed in
         # https://arxiv.org/pdf/2106.11959