diff --git a/scripts/datasets/medical/check_cbis_ddsm.py b/scripts/datasets/medical/check_cbis_ddsm.py
new file mode 100644
index 00000000..ca305183
--- /dev/null
+++ b/scripts/datasets/medical/check_cbis_ddsm.py
@@ -0,0 +1,24 @@
+from torch_em.data import MinInstanceSampler
+from torch_em.util.debug import check_loader
+from torch_em.data.datasets.medical import get_cbis_ddsm_loader
+
+
+ROOT = "/media/anwai/ANWAI/data/cbis_ddsm"
+
+
+def check_cbis_ddsm():
+    loader = get_cbis_ddsm_loader(
+        path=ROOT,
+        patch_shape=(512, 512),
+        batch_size=2,
+        split="Train",
+        task=None,
+        tumour_type=None,
+        resize_inputs=True,
+        sampler=MinInstanceSampler()
+    )
+    check_loader(loader, 8)
+
+
+if __name__ == "__main__":
+    check_cbis_ddsm()
diff --git a/torch_em/data/datasets/medical/__init__.py b/torch_em/data/datasets/medical/__init__.py
index f2ad34c7..56ba358d 100644
--- a/torch_em/data/datasets/medical/__init__.py
+++ b/torch_em/data/datasets/medical/__init__.py
@@ -2,6 +2,7 @@
 from .btcv import get_btcv_dataset, get_btcv_loader
 from .busi import get_busi_dataset, get_busi_loader
 from .camus import get_camus_dataset, get_camus_loader
+from .cbis_ddsm import get_cbis_ddsm_dataset, get_cbis_ddsm_loader
 from .cholecseg8k import get_cholecseg8k_dataset, get_cholecseg8k_loader
 from .drive import get_drive_dataset, get_drive_loader
 from .duke_liver import get_duke_liver_dataset, get_duke_liver_loader
diff --git a/torch_em/data/datasets/medical/cbis_ddsm.py b/torch_em/data/datasets/medical/cbis_ddsm.py
new file mode 100644
index 00000000..81a5830b
--- /dev/null
+++ b/torch_em/data/datasets/medical/cbis_ddsm.py
@@ -0,0 +1,120 @@
+import os
+from glob import glob
+from natsort import natsorted
+from typing import Union, Tuple, Literal, Optional
+
+import torch_em
+
+from .. import util
+
+
+def get_cbis_ddsm_data(path, split, task, tumour_type, download):
+    os.makedirs(path, exist_ok=True)
+
+    assert split in ["Train", "Test"]
+
+    if task is None:
+        task = "*"
+    else:
+        assert task in ["Calc", "Mass"]
+
+    if tumour_type is None:
+        tumour_type = "*"
+    else:
+        assert tumour_type in ["MALIGNANT", "BENIGN"]
+
+    data_dir = os.path.join(path, "DATA")
+    if os.path.exists(data_dir):
+        return os.path.join(path, "DATA", task, split, tumour_type)
+
+    util.download_source_kaggle(
+        path=path, dataset_name="mohamedbenticha/cbis-ddsm/", download=download,
+    )
+    zip_path = os.path.join(path, "cbis-ddsm.zip")
+    util.unzip(zip_path=zip_path, dst=path)
+    return os.path.join(path, "DATA", task, split, tumour_type)
+
+
+def _get_cbis_ddsm_paths(path, split, task, tumour_type, download):
+    data_dir = get_cbis_ddsm_data(
+        path=path,
+        split=split,
+        task=task,
+        tumour_type=tumour_type,
+        download=download
+    )
+
+    image_paths = natsorted(glob(os.path.join(data_dir, "*_FULL_*.png")))
+    gt_paths = natsorted(glob(os.path.join(data_dir, "*_MASK_*.png")))
+
+    assert len(image_paths) == len(gt_paths)
+
+    return image_paths, gt_paths
+
+
+def get_cbis_ddsm_dataset(
+    path: Union[os.PathLike, str],
+    patch_shape: Tuple[int, int],
+    split: Literal["Train", "Test"],
+    task: Optional[Literal["Calc", "Mass"]] = None,
+    tumour_type: Optional[Literal["MALIGNANT", "BENIGN"]] = None,
+    resize_inputs: bool = False,
+    download: bool = False,
+    **kwargs
+):
+    """Dataset for segmentation of calcification and mass in mammography.
+
+    This dataset is a preprocessed version of https://www.cancerimagingarchive.net/collection/cbis-ddsm/ available
+    at https://www.kaggle.com/datasets/mohamedbenticha/cbis-ddsm/data. The related publication is:
+    - https://doi.org/10.1038/sdata.2017.177
+
+    Please cite it if you use this dataset in a publication.
+    """
+    image_paths, gt_paths = _get_cbis_ddsm_paths(
+        path=path, split=split, task=task, tumour_type=tumour_type, download=download
+    )
+
+    if resize_inputs:
+        resize_kwargs = {"patch_shape": patch_shape, "is_rgb": False}
+        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,
+        patch_shape=patch_shape,
+        is_seg_dataset=False,
+        **kwargs
+    )
+    return dataset
+
+
+def get_cbis_ddsm_loader(
+    path: Union[os.PathLike, str],
+    patch_shape: Tuple[int, int],
+    batch_size: int,
+    split: Literal["Train", "Test"],
+    task: Optional[Literal["Calc", "Mass"]] = None,
+    tumour_type: Optional[Literal["MALIGNANT", "BENIGN"]] = None,
+    resize_inputs: bool = False,
+    download: bool = False,
+    **kwargs
+):
+    """Dataloader for segmentation of calcification and mass in mammography. See `get_cbis_ddsm_dataset` for details.
+    """
+    ds_kwargs, loader_kwargs = util.split_kwargs(torch_em.default_segmentation_dataset, **kwargs)
+    dataset = get_cbis_ddsm_dataset(
+        path=path,
+        patch_shape=patch_shape,
+        split=split,
+        task=task,
+        tumour_type=tumour_type,
+        resize_inputs=resize_inputs,
+        download=download,
+        **ds_kwargs
+    )
+    loader = torch_em.get_data_loader(dataset=dataset, batch_size=batch_size, **loader_kwargs)
+    return loader