diff --git a/src/mednet/libs/segmentation/config/data/cxr8/__init__.py b/src/mednet/libs/segmentation/config/data/cxr8/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/mednet/libs/segmentation/config/data/cxr8/datamodule.py b/src/mednet/libs/segmentation/config/data/cxr8/datamodule.py
new file mode 100644
index 0000000000000000000000000000000000000000..66faf08b553437faefbc52b5b857a0cd4fb5896d
--- /dev/null
+++ b/src/mednet/libs/segmentation/config/data/cxr8/datamodule.py
@@ -0,0 +1,129 @@
+# SPDX-FileCopyrightText: Copyright © 2024 Idiap Research Institute <contact@idiap.ch>
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+"""ChestX-ray8: Hospital-scale Chest X-ray Database."""
+
+import os
+import pathlib
+
+import numpy as np
+import PIL.Image
+import torch
+from mednet.libs.common.data.datamodule import CachingDataModule
+from mednet.libs.common.data.split import make_split
+from mednet.libs.common.data.typing import Sample
+from mednet.libs.segmentation.data.typing import (
+ SegmentationRawDataLoader as _SegmentationRawDataLoader,
+)
+from torchvision import tv_tensors
+from torchvision.transforms.functional import to_tensor
+
+from ....utils.rc import load_rc
+
+CONFIGURATION_KEY_DATADIR = "datadir." + (__name__.rsplit(".", 2)[-2])
+"""Key to search for in the configuration file for the root directory of this
+database."""
+
+CONFIGURATION_KEY_IDIAP_FILESTRUCTURE = (
+ (__name__.rsplit(".", 2)[-2]) + ".idiap_folder_structure"
+)
+"""Key to search for in the configuration file indicating if the loader should
+use standard or idiap-based file organisation structure.
+
+It causes the internal loader to search for files in a slightly
+different folder structure, that was adapted to Idiap's requirements
+(number of files per folder to be less than 10k).
+"""
+
+
+class SegmentationRawDataLoader(_SegmentationRawDataLoader):
+ """A specialized raw-data-loader for the cxr8 dataset."""
+
+ datadir: pathlib.Path
+ """This variable contains the base directory where the database raw data is
+ stored."""
+
+ def __init__(self):
+ self.datadir = pathlib.Path(
+ load_rc().get(CONFIGURATION_KEY_DATADIR, os.path.realpath(os.curdir))
+ )
+ self.idiap_file_organisation = load_rc().get(
+ CONFIGURATION_KEY_IDIAP_FILESTRUCTURE,
+ False,
+ )
+
+ def sample(self, sample: tuple[str, str, str]) -> Sample:
+ """Load a single image sample from the disk.
+
+ Parameters
+ ----------
+ sample
+ A tuple containing path suffixes to the sample image, target, and mask
+ to be loaded, within the dataset root folder.
+
+ Returns
+ -------
+ The sample representation.
+ """
+
+ file_path = pathlib.Path(sample[0])
+ label_path = pathlib.Path(sample[1])
+
+ if self.idiap_file_organisation:
+ sample_parts = sample[0].split("/", 1)
+ file_path = pathlib.Path(
+ sample_parts[0] + "/" + sample_parts[1][:5] + "/" + sample_parts[1]
+ )
+
+ label_parts = sample[1].split("/", 1)
+ label_path = pathlib.Path(
+ pathlib.Path(
+ label_parts[0] + "/" + label_parts[1][:5] + "/" + label_parts[1]
+ )
+ )
+
+ tensor = to_tensor(PIL.Image.open(self.datadir / file_path).convert(mode="RGB"))
+
+ target = np.array(
+ PIL.Image.open(
+ pathlib.Path(str(self.datadir) + "-segmentations") / label_path
+ )
+ )
+ target = np.where(target == 255, 0, target)
+ target = to_tensor(PIL.Image.fromarray(np.array(target > 0)))
+
+ tensor = tv_tensors.Image(tensor)
+ target = tv_tensors.Image(target)
+ mask = tv_tensors.Mask(torch.ones_like(target))
+
+ return tensor, dict(target=target, mask=mask, name=sample[0]) # type: ignore[arg-type]
+
+
+class DataModule(CachingDataModule):
+ """The database contains a total of 112120 images. Image size for each X-ray is
+ 1024 x 1024. One set of mask annotations is available for all images.
+
+ * Reference: [CXR8-2017]_
+ * Original resolution (height x width): 1024 x 1024
+ * Configuration resolution: 256 x 256 (after rescaling)
+ * Split reference: [GAAL-2020]_
+ * Protocol ``default``:
+
+ * Training samples: 78484 (including labels)
+ * Validation samples: 11212 (including labels)
+ * Test samples: 22424 (including labels)
+
+ Parameters
+ ----------
+ split_filename
+ Name of the .json file containing the split to load.
+ """
+
+ def __init__(self, split_filename: str):
+ assert __package__ is not None
+ super().__init__(
+ database_split=make_split(__package__, split_filename),
+ raw_data_loader=SegmentationRawDataLoader(),
+ database_name=__package__.rsplit(".", 1)[1],
+ split_name=pathlib.Path(split_filename).stem,
+ )
diff --git a/src/mednet/libs/segmentation/config/data/cxr8/default.json b/src/mednet/libs/segmentation/config/data/cxr8/default.json
new file mode 100644
index 0000000000000000000000000000000000000000..86752e103c1437239a034be81fe5fb94665197cf
--- /dev/null
+++ b/src/mednet/libs/segmentation/config/data/cxr8/default.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7125340e0645f0c1aa1b44d74b1153cb0005416e1992bd443457c9621971085d
+size 9193892
diff --git a/src/mednet/libs/segmentation/config/data/cxr8/default.py b/src/mednet/libs/segmentation/config/data/cxr8/default.py
new file mode 100644
index 0000000000000000000000000000000000000000..5efe6ac89c7dd230af04aa289d261cf99b87b03d
--- /dev/null
+++ b/src/mednet/libs/segmentation/config/data/cxr8/default.py
@@ -0,0 +1,14 @@
+# SPDX-FileCopyrightText: Copyright © 2024 Idiap Research Institute <contact@idiap.ch>
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+"""CXR8 dataset for Vessel Segmentation (default protocol).
+
+* Split reference: [CXR8-2004]_
+* This configuration resolution: 544 x 544 (center-crop)
+* See :py:mod:`deepdraw.data.cxr8` for dataset details
+* This dataset offers a second-annotator comparison for the test set only
+"""
+
+from mednet.libs.segmentation.config.data.cxr8.datamodule import DataModule
+
+datamodule = DataModule("default.json")
diff --git a/src/mednet/libs/segmentation/tests/data/histograms/models/histograms_lwnet_cxr8_default.json b/src/mednet/libs/segmentation/tests/data/histograms/models/histograms_lwnet_cxr8_default.json
new file mode 100644
index 0000000000000000000000000000000000000000..c31da578a3f284f1185e2aa4b269c29c19bca769
--- /dev/null
+++ b/src/mednet/libs/segmentation/tests/data/histograms/models/histograms_lwnet_cxr8_default.json
@@ -0,0 +1,23 @@
+{
+ "train": [
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+ ]
+}
diff --git a/src/mednet/libs/segmentation/tests/data/histograms/raw_data/histograms_cxr8_default.json b/src/mednet/libs/segmentation/tests/data/histograms/raw_data/histograms_cxr8_default.json
new file mode 100644
index 0000000000000000000000000000000000000000..627903fb18db94a2990cf575bf20f87d8f6f853f
--- /dev/null
+++ b/src/mednet/libs/segmentation/tests/data/histograms/raw_data/histograms_cxr8_default.json
@@ -0,0 +1,23 @@
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+ ]
+}
diff --git a/src/mednet/libs/segmentation/tests/test_cxr8.py b/src/mednet/libs/segmentation/tests/test_cxr8.py
new file mode 100644
index 0000000000000000000000000000000000000000..1d18d53c9bb9a1daa8d264b714e5d30cb4213d92
--- /dev/null
+++ b/src/mednet/libs/segmentation/tests/test_cxr8.py
@@ -0,0 +1,132 @@
+# SPDX-FileCopyrightText: Copyright © 2024 Idiap Research Institute <contact@idiap.ch>
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+"""Tests for cxr8 dataset."""
+
+import importlib
+
+import pytest
+from click.testing import CliRunner
+
+
+def id_function(val):
+ if isinstance(val, dict):
+ return str(val)
+ return repr(val)
+
+
+@pytest.mark.parametrize(
+ "split,lengths",
+ [
+ ("default", dict(train=78484, validation=11212, test=22424)),
+ ],
+ ids=id_function, # just changes how pytest prints it
+)
+def test_protocol_consistency(
+ database_checkers,
+ split: str,
+ lengths: dict[str, int],
+):
+ from mednet.libs.common.data.split import make_split
+
+ database_checkers.check_split(
+ make_split("mednet.libs.segmentation.config.data.cxr8", f"{split}.json"),
+ lengths=lengths,
+ )
+
+
+@pytest.mark.skip_if_rc_var_not_set("datadir.cxr8")
+def test_database_check():
+ from mednet.libs.segmentation.scripts.database import check
+
+ runner = CliRunner()
+ result = runner.invoke(check, ["--limit=10", "cxr8"])
+ assert (
+ result.exit_code == 0
+ ), f"Exit code {result.exit_code} != 0 -- Output:\n{result.output}"
+
+
+@pytest.mark.skip_if_rc_var_not_set("datadir.cxr8")
+@pytest.mark.parametrize(
+ "dataset",
+ [
+ "train",
+ "test",
+ ],
+)
+@pytest.mark.parametrize(
+ "name",
+ [
+ "default",
+ ],
+)
+def test_loading(database_checkers, name: str, dataset: str):
+ datamodule = importlib.import_module(
+ f".{name}",
+ "mednet.libs.segmentation.config.data.cxr8",
+ ).datamodule
+
+ datamodule.model_transforms = [] # should be done before setup()
+ datamodule.setup("predict") # sets up all datasets
+
+ loader = datamodule.predict_dataloader()[dataset]
+
+ limit = 3 # limit load checking
+ for batch in loader:
+ if limit == 0:
+ break
+ database_checkers.check_loaded_batch(
+ batch,
+ batch_size=1,
+ color_planes=3,
+ expected_num_targets=1,
+ )
+ limit -= 1
+
+
+@pytest.mark.skip_if_rc_var_not_set("datadir.cxr8")
+def test_raw_transforms_image_quality(database_checkers, datadir):
+ reference_histogram_file = str(
+ datadir / "histograms/raw_data/histograms_cxr8_default.json",
+ )
+
+ datamodule = importlib.import_module(
+ ".default",
+ "mednet.libs.segmentation.config.data.cxr8",
+ ).datamodule
+
+ datamodule.model_transforms = []
+ datamodule.setup("predict")
+
+ database_checkers.check_image_quality(datamodule, reference_histogram_file)
+
+
+@pytest.mark.skip_if_rc_var_not_set("datadir.cxr8")
+@pytest.mark.parametrize(
+ "model_name",
+ ["lwnet"],
+)
+def test_model_transforms_image_quality(database_checkers, datadir, model_name):
+ reference_histogram_file = str(
+ datadir / f"histograms/models/histograms_{model_name}_cxr8_default.json",
+ )
+
+ datamodule = importlib.import_module(
+ ".default",
+ "mednet.libs.segmentation.config.data.cxr8",
+ ).datamodule
+
+ model = importlib.import_module(
+ f".{model_name}",
+ "mednet.libs.segmentation.config.models",
+ ).model
+
+ datamodule.model_transforms = model.model_transforms
+ datamodule.setup("predict")
+
+ database_checkers.check_image_quality(
+ datamodule,
+ reference_histogram_file,
+ compare_type="statistical",
+ pearson_coeff_threshold=0.005,
+ )