diff --git a/scripts/datasets/medical/check_uwaterloo_skin.py b/scripts/datasets/medical/check_uwaterloo_skin.py
new file mode 100644
index 00000000..9cae6023
--- /dev/null
+++ b/scripts/datasets/medical/check_uwaterloo_skin.py
@@ -0,0 +1,21 @@
+from torch_em.util.debug import check_loader
+from torch_em.data.datasets.medical import get_uwaterloo_skin_loader
+
+
+ROOT = "/media/anwai/ANWAI/data/uwaterloo_skinseg"
+
+
+def check_uwaterloo_skin():
+    loader = get_uwaterloo_skin_loader(
+        path=ROOT,
+        patch_shape=(512, 512),
+        batch_size=2,
+        resize_inputs=True,
+        download=True,
+    )
+
+    check_loader(loader, 8)
+
+
+if __name__ == "__main__":
+    check_uwaterloo_skin()
diff --git a/torch_em/data/datasets/medical/__init__.py b/torch_em/data/datasets/medical/__init__.py
index c1fa9dbc..cb711022 100644
--- a/torch_em/data/datasets/medical/__init__.py
+++ b/torch_em/data/datasets/medical/__init__.py
@@ -11,3 +11,4 @@
 from .plethora import get_plethora_dataset, get_plethora_loader
 from .sa_med2d import get_sa_med2d_dataset, get_sa_med2d_loader
 from .siim_acr import get_siim_acr_dataset, get_siim_acr_loader
+from .uwaterloo_skin import get_uwaterloo_skin_dataset, get_uwaterloo_skin_loader
diff --git a/torch_em/data/datasets/medical/uwaterloo_skin.py b/torch_em/data/datasets/medical/uwaterloo_skin.py
new file mode 100644
index 00000000..12d56021
--- /dev/null
+++ b/torch_em/data/datasets/medical/uwaterloo_skin.py
@@ -0,0 +1,118 @@
+import os
+import shutil
+from glob import glob
+from typing import Tuple, Union
+from urllib.parse import urljoin
+from urllib3.exceptions import ProtocolError
+
+import torch_em
+
+from .. import util
+
+
+BASE_URL = "https://uwaterloo.ca/vision-image-processing-lab/sites/ca.vision-image-processing-lab/files/uploads/files/"
+
+
+ZIPFILES = {
+    "set1": "skin_image_data_set-1.zip",  # patients with melanoma
+    "set2": "skin_image_data_set-2.zip"  # patients without melanoma
+}
+
+CHECKSUMS = {
+    "set1": "1788cd3eb7a4744012aad9a154e514fc5b82b9f3b19e31cc1b6ded5fc6bed297",
+    "set2": "108a818baf20b36ef4544ebda10a8075dad99e335f0535c9533bb14cb02b5c53"
+}
+
+
+def get_uwaterloo_skin_data(path, chosen_set, download):
+    os.makedirs(path, exist_ok=True)
+
+    assert chosen_set in ZIPFILES.keys(), f"'{chosen_set}' is not a valid set."
+
+    data_dir = os.path.join(path, f"{chosen_set}_Data")
+    if os.path.exists(data_dir):
+        return data_dir
+
+    zip_path = os.path.join(path, ZIPFILES[chosen_set])
+    url = urljoin(BASE_URL, ZIPFILES[chosen_set])
+
+    try:
+        util.download_source(path=zip_path, url=url, download=download, checksum=CHECKSUMS[chosen_set])
+    except ProtocolError:  # the 'uwaterloo.ca' quite randomly times out of connections, pretty weird.
+        msg = "The server seems to be unreachable at the moment. "
+        msg += f"We recommend downloading the data manually, from '{url}' at '{path}'. "
+        print(msg)
+        quit()
+
+    util.unzip(zip_path=zip_path, dst=path)
+
+    setnum = chosen_set[-1]
+    tmp_dir = os.path.join(path, fr"Skin Image Data Set-{setnum}")
+    shutil.move(src=tmp_dir, dst=data_dir)
+
+    return data_dir
+
+
+def _get_uwaterloo_skin_paths(path, download):
+    data_dir = get_uwaterloo_skin_data(path=path, chosen_set="set1", download=download)
+
+    image_paths = sorted(glob(os.path.join(data_dir, "skin_data", "melanoma", "*", "*_orig.jpg")))
+    gt_paths = sorted(glob(os.path.join(data_dir, "skin_data", "melanoma", "*", "*_contour.png")))
+
+    data_dir = get_uwaterloo_skin_data(path=path, chosen_set="set2", download=download)
+
+    image_paths.extend(sorted(glob(os.path.join(data_dir, "skin_data", "notmelanoma", "*", "*_orig.jpg"))))
+    gt_paths.extend(sorted(glob(os.path.join(data_dir, "skin_data", "notmelanoma", "*", "*_contour.png"))))
+
+    return image_paths, gt_paths
+
+
+def get_uwaterloo_skin_dataset(
+    path: Union[os.PathLike, str],
+    patch_shape: Tuple[int, int],
+    resize_inputs: bool = False,
+    download: bool = False,
+    **kwargs
+):
+    """Dataset for skin lesion segmentation in dermoscopy images.
+
+    The database is located at https://uwaterloo.ca/vision-image-processing-lab/research-demos/skin-cancer-detection.
+    Please cite it if you use this dataset for a publication.
+    """
+    image_paths, gt_paths = _get_uwaterloo_skin_paths(path=path, download=download)
+
+    if resize_inputs:
+        resize_kwargs = {"patch_shape": patch_shape, "is_rgb": True}
+        kwargs, patch_shape = util.update_kwargs_for_resize_trafo(
+            kwargs=kwargs, patch_shape=patch_shape, resize_inputs=resize_inputs, resize_kwargs=resize_kwargs
+        )
+
+    dataset = torch_em.default_segmentation_dataset(
+        raw_paths=image_paths,
+        raw_key=None,
+        label_paths=gt_paths,
+        label_key=None,
+        is_seg_dataset=False,
+        patch_shape=patch_shape,
+        **kwargs
+    )
+
+    return dataset
+
+
+def get_uwaterloo_skin_loader(
+    path: Union[os.PathLike, str],
+    patch_shape: Tuple[int, int],
+    batch_size: int,
+    resize_inputs: bool = False,
+    download: bool = False,
+    **kwargs
+):
+    """Dataset for skin lesion segmentation in dermoscopy images. See `get_uwaterloo_skin_dataset` for details.
+    """
+    ds_kwargs, loader_kwargs = util.split_kwargs(torch_em.default_segmentation_dataset, **kwargs)
+    dataset = get_uwaterloo_skin_dataset(
+        path=path, patch_shape=patch_shape, resize_inputs=resize_inputs, download=download, **ds_kwargs
+    )
+    loader = torch_em.get_data_loader(dataset=dataset, batch_size=batch_size, **loader_kwargs)
+    return loader