diff --git a/src/classpose/entrypoints/predict_wsi.py b/src/classpose/entrypoints/predict_wsi.py
index b31f883..bb57a8d 100644
--- a/src/classpose/entrypoints/predict_wsi.py
+++ b/src/classpose/entrypoints/predict_wsi.py
@@ -303,6 +303,11 @@ def fill_queue(self):
         """
         self._init_slide()
         self._get_tissue_contours()
+        if self.tissue_detection_model_path is not None and not self.tissue_cnts:
+            logger.warning("No tissue detected in slide. Skipping inference.")
+            for _ in range(self.n_none):
+                self.q.put((None, None))
+            return
         n = 0
         with tqdm(self.coords, desc="Tiles to predict: 0", position=0) as pbar:
             for coords, tile_size in pbar:
diff --git a/src/classpose/grandqc/wsi_tissue_detection.py b/src/classpose/grandqc/wsi_tissue_detection.py
index e950572..7dec55e 100644
--- a/src/classpose/grandqc/wsi_tissue_detection.py
+++ b/src/classpose/grandqc/wsi_tissue_detection.py
@@ -210,6 +210,15 @@ def detect_tissue_wsi(
         cv2.CHAIN_APPROX_SIMPLE,
     )
 
+    if hierarchy is None:
+        grandqc_logger.warning("No tissue contours detected in slide.")
+        empty_geojson = {"type": "FeatureCollection", "features": []}
+        filled_class_map = np.zeros_like(end_image_class_map)
+        del model
+        del preprocessing_fn
+        del slide
+        return (image, filtered_mask, filled_class_map, {}, empty_geojson, mpp_model_td)
+
     holes_idx = np.where(hierarchy[0, :, 3] != -1)[0]
     scaling_array = np.array([observed_reduction_w, observed_reduction_h])
     output_cnts = {}