diff --git a/tests/engines/test_patch_predictor.py b/tests/engines/test_patch_predictor.py
index cd88ec073..8f62f5037 100644
--- a/tests/engines/test_patch_predictor.py
+++ b/tests/engines/test_patch_predictor.py
@@ -446,6 +446,46 @@ def test_engine_run_wsi_annotation_store(
     shutil.rmtree(save_dir)
 
 
+def test_engine_run_wsi_annotation_store_power(
+    sample_wsi_dict: dict,
+    tmp_path: Path,
+) -> None:
+    """Test the engine run for Whole slide images."""
+    # convert to pathlib Path to prevent wsireader complaint
+    mini_wsi_svs = Path(sample_wsi_dict["wsi2_4k_4k_svs"])
+    mini_wsi_msk = Path(sample_wsi_dict["wsi2_4k_4k_msk"])
+
+    eng = PatchPredictor(model="alexnet-kather100k")
+
+    patch_size = np.array([224, 224])
+    save_dir = f"{tmp_path}/model_wsi_output"
+
+    kwargs = {
+        "patch_input_shape": patch_size,
+        "stride_shape": patch_size,
+        "resolution": 20,
+        "save_dir": save_dir,
+        "units": "power",
+    }
+
+    output = eng.run(
+        images=[mini_wsi_svs],
+        masks=[mini_wsi_msk],
+        patch_mode=False,
+        output_type="AnnotationStore",
+        **kwargs,
+    )
+
+    output_ = output[mini_wsi_svs]
+
+    assert output_.exists()
+    assert output_.suffix == ".db"
+    predictions = _extract_probabilities_from_annotation_store(output_)
+    assert _validate_probabilities(predictions)
+
+    shutil.rmtree(save_dir)
+
+
 # -------------------------------------------------------------------------------------
 # Command Line Interface
 # -------------------------------------------------------------------------------------
diff --git a/tiatoolbox/models/engine/engine_abc.py b/tiatoolbox/models/engine/engine_abc.py
index e8f3c63f6..465230116 100644
--- a/tiatoolbox/models/engine/engine_abc.py
+++ b/tiatoolbox/models/engine/engine_abc.py
@@ -1053,7 +1053,7 @@ def _calculate_scale_factor(dataloader: DataLoader) -> float | tuple[float, floa
         # in this case dataloader resolution / slide resolution will be
         # equal to dataloader resolution.
 
-        if dataloader_units in ["mpp", "level", "objective_power"]:
+        if dataloader_units in ["mpp", "level", "power"]:
             wsimeta_dict = dataloader.dataset.reader.info.as_dict()
 
         if dataloader_units == "mpp":
@@ -1065,8 +1065,8 @@ def _calculate_scale_factor(dataloader: DataLoader) -> float | tuple[float, floa
             downsample_ratio = wsimeta_dict["level_downsamples"][dataloader_resolution]
             return 1.0 / downsample_ratio, 1.0 / downsample_ratio
 
-        if dataloader_resolution == "objective_power":
-            slide_objective_power = wsimeta_dict["power"]
+        if dataloader_units == "power":
+            slide_objective_power = wsimeta_dict["objective_power"]
             return (
                 dataloader_resolution / slide_objective_power,
                 dataloader_resolution / slide_objective_power,