diff --git a/scripts/inference.py b/scripts/inference.py
index de46157..714102a 100644
--- a/scripts/inference.py
+++ b/scripts/inference.py
@@ -14,21 +14,45 @@
 def _get_parser():
     parser = argparse.ArgumentParser(description=__doc__)
 
-    parser.add_argument('--output-dir', default="sybil_result", dest="output_dir",
-                        help="Output directory in which to save prediction results."
-                             "Prediction will be printed to stdout as well.")
-
-    parser.add_argument('--return-attentions', default=False, action="store_true",
-                        help="Generate an image which overlaps attention scores.")
-
-    parser.add_argument('dicom_dir', default=None,
-                        help="Path to directory containing DICOM files (from a single exam) to run inference on."
-                             "Every dcm file in the directory will be included.")
-
-    parser.add_argument('--model-name', default="sybil_ensemble", dest="model_name",
-                        help="Name of the model to use for prediction. Default: sybil_ensemble")
-
-    parser.add_argument('-l', '--log', '--loglevel', default="INFO", dest="loglevel")
+    parser.add_argument(
+        "image_dir",
+        default=None,
+        help="Path to directory containing DICOM/PNG files (from a single exam) to run inference on."
+             "Every file in the directory will be included.",
+    )
+
+    parser.add_argument(
+        "--output-dir",
+        default="sybil_result",
+        dest="output_dir",
+        help="Output directory in which to save prediction results."
+        "Prediction will be printed to stdout as well.",
+    )
+
+    parser.add_argument(
+        "--return-attentions",
+        default=False,
+        action="store_true",
+        help="Generate an image which overlaps attention scores.",
+    )
+
+    parser.add_argument(
+        "--file-type",
+        default="auto",
+        dest="file_type",
+        choices={"dicom", "png", "auto"},
+        help="File type of input images. "
+        "If not provided, the file type will be inferred from input extensions.",
+    )
+
+    parser.add_argument(
+        "--model-name",
+        default="sybil_ensemble",
+        dest="model_name",
+        help="Name of the model to use for prediction. Default: sybil_ensemble",
+    )
+
+    parser.add_argument("-l", "--log", "--loglevel", default="INFO", dest="loglevel")
 
     return parser
 
@@ -38,26 +62,45 @@ def logging_basic_config(args):
     debug_fmt = "[%(asctime)s] [%(filename)s:%(lineno)d] %(levelname)s - %(message)s"
     fmt = debug_fmt if args.loglevel.upper() == "DEBUG" else info_fmt
 
-    logging.basicConfig(format=fmt,
-                        datefmt="%Y-%m-%d %H:%M:%S",
-                        level=args.loglevel.upper())
-
+    logging.basicConfig(
+        format=fmt, datefmt="%Y-%m-%d %H:%M:%S", level=args.loglevel.upper()
+    )
+
+
+def inference(
+    image_dir,
+    output_dir,
+    model_name="sybil_ensemble",
+    return_attentions=False,
+    file_type="auto",
+):
+    logger = logging.getLogger("inference")
+
+    input_files = os.listdir(image_dir)
+    input_files = [os.path.join(image_dir, x) for x in input_files if not x.startswith(".")]
+    input_files = [x for x in input_files if os.path.isfile(x)]
+    extensions = {os.path.splitext(x)[1] for x in input_files}
+    if len(extensions) > 1:
+        raise ValueError(
+            f"Multiple file types found in {image_dir}: {','.join(extensions)}"
+        )
 
-def inference(dicom_dir, output_dir, model_name="sybil_ensemble", return_attentions=False):
-    logger = logging.getLogger('inference')
+    if file_type == "auto":
+        extension = extensions.pop()
+        file_type = "dicom"
+        if extension.lower() in {".png", "png"}:
+            file_type = "png"
+    assert file_type in {"dicom", "png"}
 
-    dicom_files = os.listdir(dicom_dir)
-    dicom_files = [os.path.join(dicom_dir, x) for x in dicom_files]
-    dicom_files = [x for x in dicom_files if x.endswith(".dcm") and os.path.isfile(x)]
-    num_files = len(dicom_files)
+    num_files = len(input_files)
 
     # Load a trained model
     model = Sybil(model_name)
 
-    logger.debug(f"Beginning prediction using {num_files} files from {dicom_dir}")
+    logger.debug(f"Beginning prediction using {num_files} {file_type} files from {image_dir}")
 
     # Get risk scores
-    serie = Serie(dicom_files)
+    serie = Serie(input_files, file_type=file_type)
     series = [serie]
     prediction = model.predict(series, return_attentions=return_attentions)
     prediction_scores = prediction.scores[0]
@@ -90,7 +133,13 @@ def main():
 
     os.makedirs(args.output_dir, exist_ok=True)
 
-    pred_dict = inference(args.dicom_dir, args.output_dir, args.model_name, args.return_attentions)
+    pred_dict = inference(
+        args.image_dir,
+        args.output_dir,
+        args.model_name,
+        args.return_attentions,
+        file_type=args.file_type,
+    )
 
     print(json.dumps(pred_dict, indent=2))