diff --git a/ivadomed/scripts/automate_training.py b/ivadomed/scripts/automate_training.py
index 8bdc1db8b..048c86e35 100755
--- a/ivadomed/scripts/automate_training.py
+++ b/ivadomed/scripts/automate_training.py
@@ -17,7 +17,6 @@
 from functools import partial
 import json
 import logging
-import os
 import random
 import collections.abc
 import shutil
@@ -27,6 +26,7 @@
 import numpy as np
 import torch.multiprocessing as mp
 import ivadomed.scripts.visualize_and_compare_testing_models as violin_plots
+from pathlib import Path
 from ivadomed import main as ivado
 from ivadomed import config_manager as imed_config_manager
 from ivadomed.loader import utils as imed_loader_utils
@@ -153,14 +153,14 @@ def split_dataset(initial_config):
                 }
     """
     loader_parameters = initial_config["loader_parameters"]
-    path_output = initial_config["path_output"]
-    if not os.path.isdir(path_output):
+    path_output = Path(initial_config["path_output"])
+    if not path_output.is_dir():
         print('Creating output path: {}'.format(path_output))
-        os.makedirs(path_output)
+        path_output.mkdir(parents=True)
     else:
         print('Output path already exists: {}'.format(path_output))
 
-    bids_df = imed_loader_utils.BidsDataframe(loader_parameters, path_output, derivatives=True)
+    bids_df = imed_loader_utils.BidsDataframe(loader_parameters, str(path_output), derivatives=True)
 
     train_lst, valid_lst, test_lst = imed_loader_utils.get_new_subject_file_split(
         df=bids_df.df,
@@ -654,8 +654,8 @@ def automate_training(file_config, file_config_hyper, fixed_split, all_combin, p
             applied, then all the second, etc.
         output_dir (str): Path to where the results will be saved.
     """
-    if output_dir and not os.path.exists(output_dir):
-        os.makedirs(output_dir)
+    if output_dir and not Path(output_dir).exists():
+        Path(output_dir).mkdir(parents=True)
     if not output_dir:
         output_dir = ""
 
@@ -670,7 +670,7 @@ def automate_training(file_config, file_config_hyper, fixed_split, all_combin, p
         initial_config = split_dataset(initial_config)
 
     # Hyperparameters values to experiment
-    with open(file_config_hyper, "r") as fhandle:
+    with Path(file_config_hyper).open(mode="r") as fhandle:
         config_hyper = json.load(fhandle)
 
     param_list = get_param_list(config_hyper, [], [])
@@ -711,16 +711,16 @@ def automate_training(file_config, file_config_hyper, fixed_split, all_combin, p
                 new_config_list = []
                 for config in config_list:
                     # Delete path_pred
-                    path_pred = os.path.join(config['path_output'], 'pred_masks')
-                    if os.path.isdir(path_pred) and n_iterations > 1:
+                    path_pred = Path(config['path_output'], 'pred_masks')
+                    if path_pred.is_dir() and n_iterations > 1:
                         try:
-                            shutil.rmtree(path_pred)
+                            shutil.rmtree(str(path_pred))
                         except OSError as e:
                             logging.info("Error: %s - %s." % (e.filename, e.strerror))
 
                     # Take the config file within the path_output because binarize_prediction may have been updated
-                    json_path = os.path.join(config['path_output'], 'config_file.json')
-                    new_config = imed_config_manager.ConfigurationManager(json_path).get_config()
+                    json_path = Path(config['path_output'], 'config_file.json')
+                    new_config = imed_config_manager.ConfigurationManager(str(json_path)).get_config()
                     new_config["gpu_ids"] = config["gpu_ids"]
                     new_config_list.append(new_config)
 
@@ -761,11 +761,11 @@ def automate_training(file_config, file_config_hyper, fixed_split, all_combin, p
                 combined_df = val_df
 
             results_df = pd.concat([results_df, combined_df])
-            results_df.to_csv(os.path.join(output_dir, "temporary_results.csv"))
-            eval_df.to_csv(os.path.join(output_dir, "average_eval.csv"))
+            results_df.to_csv(str(Path(output_dir, "temporary_results.csv")))
+            eval_df.to_csv(str(Path(output_dir, "average_eval.csv")))
 
     results_df = format_results(results_df, config_list, param_list)
-    results_df.to_csv(os.path.join(output_dir, "detailed_results.csv"))
+    results_df.to_csv(str(Path(output_dir, "detailed_results.csv")))
 
     logging.info("Detailed results")
     logging.info(results_df)