[doc] chore: update doc

MedVision.py

@@ -31,12 +31,20 @@ RAMDOM_SEED = 1024
 SPLIT_TRAIN_RATIO = 0.7
 
 _CITATION = """\
-Please cite the paper if you use the MedVision dataset:
-- (to be updated)
+@misc{yao2025medvisiondatasetbenchmarkquantitative,
+      title={MedVision: Dataset and Benchmark for Quantitative Medical Image Analysis}, 
+      author={Yongcheng Yao and Yongshuo Zong and Raman Dutt and Yongxin Yang and Sotirios A Tsaftaris and Timothy Hospedales},
+      year={2025},
+      eprint={2511.18676},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2511.18676}, 
+}
 """
 
 _DESCRIPTION = """\
 This is the official release of the MedVision dataset.
+Project: https://medvision-vlm.github.io
 """
 
 _HOME_PAGE = "https://huggingface.co/datasets/YongchengYAO/MedVision"

README.md

@@ -114,7 +114,7 @@ For essential updates, check the [change log](https://huggingface.co/datasets/Yo
 
 <br/>
 
-# Add New Datasets
+# New Datasets Guide
 To add new datasets, check this [blog](https://huggingface.co/blog/YongchengYAO/medvision-dataset) for an introduction of MedVision dataset.
 
 # Requirement
@@ -163,6 +163,370 @@ export MedVision_FORCE_INSTALL_CODE="False"
 
 <br/>
 
+# 📖 Essential Dataset Concept
+
+We cover some essential concepts that help we use the MedVision dataset with ease.
+
+## Concepts: Dataset & Data Configuration
+
+- `MedVision`: the collection of public imaging data and our annotations
+- `dataset`: name of the public datasets, such `BraTS24`, `MSD`, `OAIZIB-CM`
+- `data-config`: name of predefined subsets
+  - naming convention: `{dataset}_{annotation-type}_{task-ID}_{slice}_{split}`
+    - `dataset`: [details](https://huggingface.co/datasets/YongchengYAO/MedVision#datasets)
+    - `annotation-type`: 
+      - `BoxSize`: detection annotations (bounding box)
+      - `TumorLesionSize`: tumor/lesion size annotations
+      - `BiometricsFromLandmarks`: angle/distance annotations
+    - `task-ID`: `Task[xx]` (Note, this is a local ID in the dataset, not a glocal ID in MedVision.)
+      - For datasets with multiple image-mask pairs, we defined tasks in `medvision_ds/datasets/*/preprocess_*.py`
+      - source: [medvision_ds](https://huggingface.co/datasets/YongchengYAO/MedVision/tree/main/src)
+      - e.g., detection tasks for the `BraTS24` dataset is defined in the `benchmark_plan` in `medvision_ds/datasets/BraTS24/preprocess_detection.py`
+    - `slice`: [`Sagittal`, `Coronal`, `Axial`]
+    - `split`: [`Train`, `Test`]
+
+## What's returned from MedVision Dataset?
+
+We only share the annotations (https://huggingface.co/datasets/YongchengYAO/MedVision/tree/main/Datasets). The data loading script [`MedVision.py`](https://huggingface.co/datasets/YongchengYAO/MedVision/blob/main/MedVision.py) will handle raw image downloading and processing. The returned fields in each sample is defined as followed.
+
+⚠️ In `MedVision.py`, the class `MedVision(GeneratorBasedBuilder)` defines the feature dict and the method `_generate_examples()` builds the dataset.
+
+<details>
+<summary>Code block in `MedVision(GeneratorBasedBuilder)` (Click to expand)</summary>
+
+  ``` python
+  """
+  MedVision dataset.
+
+  NOTE: To update the features returned by the load_dataset() method, the followings should be updated:
+          - the feature dict in this class 
+          - the dict yielded by the _generate_examples() method 
+  """
+
+  # The feature dict for the task:
+  # - Mask-Size
+  features_dict_MaskSize = {
+      "dataset_name": Value("string"),
+      "taskID": Value("string"),
+      "taskType": Value("string"),
+      "image_file": Value("string"),
+      "mask_file": Value("string"),
+      "slice_dim": Value("uint8"),
+      "slice_idx": Value("uint16"),
+      "label": Value("uint16"),
+      "image_size_2d": Sequence(Value("uint16"), length=2),
+      "pixel_size": Sequence(Value("float16"), length=2),
+      "image_size_3d": Sequence(Value("uint16"), length=3),
+      "voxel_size": Sequence(Value("float16"), length=3),
+      "pixel_count": Value("uint32"),
+      "ROI_area": Value("float16"),
+  }
+
+  # The feature dict for the task:
+  # - Box-Size
+  features_dict_BoxSize = {
+      "dataset_name": Value("string"),
+      "taskID": Value("string"),
+      "taskType": Value("string"),
+      "image_file": Value("string"),
+      "mask_file": Value("string"),
+      "slice_dim": Value("uint8"),
+      "slice_idx": Value("uint16"),
+      "label": Value("uint16"),
+      "image_size_2d": Sequence(Value("uint16"), length=2),
+      "pixel_size": Sequence(Value("float16"), length=2),
+      "image_size_3d": Sequence(Value("uint16"), length=3),
+      "voxel_size": Sequence(Value("float16"), length=3),
+      "bounding_boxes": Sequence(
+          {
+              "min_coords": Sequence(Value("uint16"), length=2),
+              "max_coords": Sequence(Value("uint16"), length=2),
+              "center_coords": Sequence(Value("uint16"), length=2),
+              "dimensions": Sequence(Value("uint16"), length=2),
+              "sizes": Sequence(Value("float16"), length=2),
+          },
+      ),
+  }
+
+  features_dict_BiometricsFromLandmarks = {
+      "dataset_name": Value("string"),
+      "taskID": Value("string"),
+      "taskType": Value("string"),
+      "image_file": Value("string"),
+      "landmark_file": Value("string"),
+      "slice_dim": Value("uint8"),
+      "slice_idx": Value("uint16"),
+      "image_size_2d": Sequence(Value("uint16"), length=2),
+      "pixel_size": Sequence(Value("float16"), length=2),
+      "image_size_3d": Sequence(Value("uint16"), length=3),
+      "voxel_size": Sequence(Value("float16"), length=3),
+      "biometric_profile": {
+          "metric_type": Value("string"),
+          "metric_map_name": Value("string"),
+          "metric_key": Value("string"),
+          "metric_value": Value("float16"),
+          "metric_unit": Value("string"),
+          "slice_dim": Value("uint8"),
+      },
+  }
+
+  features_dict_TumorLesionSize = {
+      "dataset_name": Value("string"),
+      "taskID": Value("string"),
+      "taskType": Value("string"),
+      "image_file": Value("string"),
+      "landmark_file": Value("string"),
+      "mask_file": Value("string"),
+      "slice_dim": Value("uint8"),
+      "slice_idx": Value("uint16"),
+      "label": Value("uint16"),
+      "image_size_2d": Sequence(Value("uint16"), length=2),
+      "pixel_size": Sequence(Value("float16"), length=2),
+      "image_size_3d": Sequence(Value("uint16"), length=3),
+      "voxel_size": Sequence(Value("float16"), length=3),
+      "biometric_profile": Sequence(
+          {
+              "metric_type": Value("string"),
+              "metric_map_name": Value("string"),
+              "metric_key_major_axis": Value("string"),
+              "metric_value_major_axis": Value("float16"),
+              "metric_key_minor_axis": Value("string"),
+              "metric_value_minor_axis": Value("float16"),
+              "metric_unit": Value("string"),
+          },
+      ),
+  }
+  ```
+</details>
+
+<details>
+<summary>Code block in `_generate_examples` (Click to expand)</summary>
+
+  ```python
+  # Task type: Mask-Size
+  if taskType == "Mask-Size":
+      flatten_slice_profiles = (
+          MedVision_BenchmarkPlannerSegmentation.flatten_slice_profiles_2d
+      )
+      if imageSliceType.lower() == "sagittal":
+          slice_dim = 0
+      elif imageSliceType.lower() == "coronal":
+          slice_dim = 1
+      elif imageSliceType.lower() == "axial":
+          slice_dim = 2
+      slice_profile_flattened = flatten_slice_profiles(biometricData, slice_dim)
+      for idx, case in enumerate(slice_profile_flattened):
+          # Skip cases with a mask size smaller than 200 pixels
+          if case["pixel_count"] < 200:
+              continue
+          else:
+              yield idx, {
+                  "dataset_name": dataset_name,
+                  "taskID": taskID,
+                  "taskType": taskType,
+                  "image_file": os.path.join(dataset_dir, case["image_file"]),
+                  "mask_file": os.path.join(dataset_dir, case["mask_file"]),
+                  "slice_dim": case["slice_dim"],
+                  "slice_idx": case["slice_idx"],
+                  "label": case["label"],
+                  "image_size_2d": case["image_size_2d"],
+                  "pixel_size": case["pixel_size"],
+                  "image_size_3d": case["image_size_3d"],
+                  "voxel_size": case["voxel_size"],
+                  "pixel_count": case["pixel_count"],
+                  "ROI_area": case["ROI_area"],
+              }
+
+  # Task type: Box-Size
+  if taskType == "Box-Size":
+      if imageType.lower() == "2d":
+          flatten_slice_profiles = (
+              MedVision_BenchmarkPlannerDetection.flatten_slice_profiles_2d
+          )
+          if imageSliceType.lower() == "sagittal":
+              slice_dim = 0
+          elif imageSliceType.lower() == "coronal":
+              slice_dim = 1
+          elif imageSliceType.lower() == "axial":
+              slice_dim = 2
+          slice_profile_flattened = flatten_slice_profiles(
+              biometricData, slice_dim
+          )
+          for idx, case in enumerate(slice_profile_flattened):
+              # Skip cases with multiple bounding boxes in the same slice
+              if len(case["bounding_boxes"]) > 1:
+                  continue
+              # Skip cases with a bounding box size smaller than 10 pixels in any dimension
+              elif (
+                  case["bounding_boxes"][0]["dimensions"][0] < 10
+                  or case["bounding_boxes"][0]["dimensions"][1] < 10
+              ):
+                  continue
+              else:
+                  yield idx, {
+                      "dataset_name": dataset_name,
+                      "taskID": taskID,
+                      "taskType": taskType,
+                      "image_file": os.path.join(dataset_dir, case["image_file"]),
+                      "mask_file": os.path.join(dataset_dir, case["mask_file"]),
+                      "slice_dim": case["slice_dim"],
+                      "slice_idx": case["slice_idx"],
+                      "label": case["label"],
+                      "image_size_2d": case["image_size_2d"],
+                      "pixel_size": case["pixel_size"],
+                      "image_size_3d": case["image_size_3d"],
+                      "voxel_size": case["voxel_size"],
+                      "bounding_boxes": case["bounding_boxes"],
+                  }
+
+  # Task type: Biometrics-From-Landmarks
+  if taskType == "Biometrics-From-Landmarks":
+      if imageType.lower() == "2d":
+          flatten_slice_profiles = (
+              MedVision_BenchmarkPlannerBiometry.flatten_slice_profiles_2d
+          )
+          if imageSliceType.lower() == "sagittal":
+              slice_dim = 0
+          elif imageSliceType.lower() == "coronal":
+              slice_dim = 1
+          elif imageSliceType.lower() == "axial":
+              slice_dim = 2
+          slice_profile_flattened = flatten_slice_profiles(
+              biometricData, slice_dim
+          )
+          for idx, case in enumerate(slice_profile_flattened):
+              yield idx, {
+                  "dataset_name": dataset_name,
+                  "taskID": taskID,
+                  "taskType": taskType,
+                  "image_file": os.path.join(dataset_dir, case["image_file"]),
+                  "landmark_file": os.path.join(
+                      dataset_dir, case["landmark_file"]
+                  ),
+                  "slice_dim": case["slice_dim"],
+                  "slice_idx": case["slice_idx"],
+                  "image_size_2d": case["image_size_2d"],
+                  "pixel_size": case["pixel_size"],
+                  "image_size_3d": case["image_size_3d"],
+                  "voxel_size": case["voxel_size"],
+                  "biometric_profile": case["biometric_profile"],
+              }
+
+  # Task type: Biometrics-From-Landmarks-Distance
+  if taskType == "Biometrics-From-Landmarks-Distance":
+      if imageType.lower() == "2d":
+          flatten_slice_profiles = (
+              MedVision_BenchmarkPlannerBiometry.flatten_slice_profiles_2d
+          )
+          if imageSliceType.lower() == "sagittal":
+              slice_dim = 0
+          elif imageSliceType.lower() == "coronal":
+              slice_dim = 1
+          elif imageSliceType.lower() == "axial":
+              slice_dim = 2
+          slice_profile_flattened = flatten_slice_profiles(
+              biometricData, slice_dim
+          )
+          for idx, case in enumerate(slice_profile_flattened):
+              if case["biometric_profile"]["metric_type"] == "distance":
+                  yield idx, {
+                      "dataset_name": dataset_name,
+                      "taskID": taskID,
+                      "taskType": taskType,
+                      "image_file": os.path.join(dataset_dir, case["image_file"]),
+                      "landmark_file": os.path.join(
+                          dataset_dir, case["landmark_file"]
+                      ),
+                      "slice_dim": case["slice_dim"],
+                      "slice_idx": case["slice_idx"],
+                      "image_size_2d": case["image_size_2d"],
+                      "pixel_size": case["pixel_size"],
+                      "image_size_3d": case["image_size_3d"],
+                      "voxel_size": case["voxel_size"],
+                      "biometric_profile": case["biometric_profile"],
+                  }
+
+  # Task type: Biometrics-From-Landmarks-Angle
+  if taskType == "Biometrics-From-Landmarks-Angle":
+      if imageType.lower() == "2d":
+          flatten_slice_profiles = (
+              MedVision_BenchmarkPlannerBiometry.flatten_slice_profiles_2d
+          )
+          if imageSliceType.lower() == "sagittal":
+              slice_dim = 0
+          elif imageSliceType.lower() == "coronal":
+              slice_dim = 1
+          elif imageSliceType.lower() == "axial":
+              slice_dim = 2
+          slice_profile_flattened = flatten_slice_profiles(
+              biometricData, slice_dim
+          )
+          for idx, case in enumerate(slice_profile_flattened):
+              if case["biometric_profile"]["metric_type"] == "angle":
+                  yield idx, {
+                      "dataset_name": dataset_name,
+                      "taskID": taskID,
+                      "taskType": taskType,
+                      "image_file": os.path.join(dataset_dir, case["image_file"]),
+                      "landmark_file": os.path.join(
+                          dataset_dir, case["landmark_file"]
+                      ),
+                      "slice_dim": case["slice_dim"],
+                      "slice_idx": case["slice_idx"],
+                      "image_size_2d": case["image_size_2d"],
+                      "pixel_size": case["pixel_size"],
+                      "image_size_3d": case["image_size_3d"],
+                      "voxel_size": case["voxel_size"],
+                      "biometric_profile": case["biometric_profile"],
+                  }
+
+  # Task type: Tumor-Lesion-Size
+  if taskType == "Tumor-Lesion-Size":
+      if imageType.lower() == "2d":
+          # Get the target label for the task
+          target_label = benchmark_plan["tasks"][int(taskID) - 1]["target_label"]
+
+          flatten_slice_profiles = (
+              MedVision_BenchmarkPlannerBiometry_fromSeg.flatten_slice_profiles_2d
+          )
+          if imageSliceType.lower() == "sagittal":
+              slice_dim = 0
+          elif imageSliceType.lower() == "coronal":
+              slice_dim = 1
+          elif imageSliceType.lower() == "axial":
+              slice_dim = 2
+          slice_profile_flattened = flatten_slice_profiles(
+              biometricData, slice_dim
+          )
+          for idx, case in enumerate(slice_profile_flattened):
+              # Skip cases with multiple fitted ellipses in the same slice
+              if len(case["biometric_profile"]) > 1:
+                  continue
+              else:
+                  yield idx, {
+                      "dataset_name": dataset_name,
+                      "taskID": taskID,
+                      "taskType": taskType,
+                      "image_file": os.path.join(dataset_dir, case["image_file"]),
+                      "mask_file": os.path.join(dataset_dir, case["mask_file"]),
+                      "landmark_file": os.path.join(
+                          dataset_dir, case["landmark_file"]
+                      ),
+                      "slice_dim": case["slice_dim"],
+                      "slice_idx": case["slice_idx"],
+                      "label": target_label,
+                      "image_size_2d": case["image_size_2d"],
+                      "pixel_size": case["pixel_size"],
+                      "image_size_3d": case["image_size_3d"],
+                      "voxel_size": case["voxel_size"],
+                      "biometric_profile": case["biometric_profile"],
+                  }
+
+  ```
+</details>
+
+<br/>
+
 # Advanced Usage
 
 The dataset codebase `medvision_ds` can be used to scale the dataset, including adding new annotation types and datasets.

doc/changelog.md

@@ -2,6 +2,7 @@
 
 This is a summary of essential changes.
 
+- [Jan, 2026] [feat] add 'dataset_name' to each sample @ [15c83e077dd05f3f7358366d23636f62fa824fe1](https://huggingface.co/datasets/YongchengYAO/MedVision/commit/15c83e077dd05f3f7358366d23636f62fa824fe1) and [b4bc3cb31521b3e95d20bf55b6701b234b7101db](https://huggingface.co/datasets/YongchengYAO/MedVision/commit/b4bc3cb31521b3e95d20bf55b6701b234b7101db)
 - [Jan, 2026] [chore] fix typo in label name of AMOS22: arota --> aorta @ [25b03408a120f14ee1d620841a79c3ca5fda8f54](https://huggingface.co/datasets/YongchengYAO/MedVision/commit/25b03408a120f14ee1d620841a79c3ca5fda8f54)
 - [Dec, 2025] [feat] Add filelock mechanism to dataset cache to prevent race conditions @ [5d2731ad873611e7011053f89cfedd4109142c32](https://huggingface.co/datasets/YongchengYAO/MedVision/commit/5d2731ad873611e7011053f89cfedd4109142c32)
 - [Oct, 2025] update: set default data source of BCV15 and BraTS24 to corresponding HF dataset @ [111e3602b1f8633d17b28de4e89a44aae0430fc0](https://huggingface.co/datasets/YongchengYAO/MedVision/commit/111e3602b1f8633d17b28de4e89a44aae0430fc0)