diff --git a/tests/data/sampler/test_uniform_sampler.py b/tests/data/sampler/test_uniform_sampler.py
index d375d6ea0..54982133b 100644
--- a/tests/data/sampler/test_uniform_sampler.py
+++ b/tests/data/sampler/test_uniform_sampler.py
@@ -14,16 +14,6 @@ def test_uniform_probabilities(self):
         fixtures = torch.ones_like(probabilities)
         assert torch.all(probabilities.eq(fixtures))
 
-    def test_processed_uniform_probabilities(self):
-        sampler = UniformSampler(5)
-        probabilities = sampler.get_probability_map(self.sample)
-        probabilities = sampler.process_probability_map(probabilities)
-        fixtures = np.zeros_like(probabilities)
-        # Other positions cannot be patch centers
-        fixtures[2:-2, 2:-2, 2:-2] = probabilities[2, 2, 2]
-        self.assertAlmostEqual(probabilities.sum(), 1)
-        assert np.equal(probabilities, fixtures).all()
-
     def test_incosistent_shape(self):
         # https://github.com/fepegar/torchio/issues/234#issuecomment-675029767
         sample = torchio.Subject(
diff --git a/torchio/data/sampler/uniform.py b/torchio/data/sampler/uniform.py
index b115c466a..50a8257ff 100644
--- a/torchio/data/sampler/uniform.py
+++ b/torchio/data/sampler/uniform.py
@@ -1,10 +1,12 @@
 import torch
 from ...data.subject import Subject
 from ...torchio import TypePatchSize
-from .weighted import WeightedSampler
+from .sampler import RandomSampler
+from typing import Optional, Tuple, Generator
+import numpy as np
 
 
-class UniformSampler(WeightedSampler):
+class UniformSampler(RandomSampler):
     """Randomly extract patches from a volume with uniform probability.
 
     Args:
@@ -15,3 +17,18 @@ def __init__(self, patch_size: TypePatchSize):
 
     def get_probability_map(self, sample: Subject) -> torch.Tensor:
         return torch.ones(1, *sample.spatial_shape)
+
+    def __call__(self, sample: Subject) -> Generator[Subject, None, None]:
+
+        sample.check_consistent_spatial_shape()
+
+        if np.any(self.patch_size > sample.spatial_shape):
+            message = (
+                f'Patch size {tuple(self.patch_size)} cannot be'
+                f' larger than image size {tuple(sample.spatial_shape)}'
+            )
+            raise RuntimeError(message)
+
+        valid_range = sample.spatial_shape - self.patch_size
+        corners = np.asarray([torch.randint(x+1,(1,)).item() for x in valid_range])
+        yield self.extract_patch(sample, corners)