Datasets:

TokyoTechMagicYang
/

RAM-H1200-v1

@@ -1,515 +1,513 @@
----
-language:
-- en
-pretty_name: RAM-H1200
-size_categories:
-- 1K<n<10K
-task_categories:
-- image-segmentation
-# - object-detection
-- image-classification
-task_ids:
-- semantic-segmentation
-- instance-segmentation
-- object-detection
-- multi-class-classification
-tags:
-- medical
-- radiography
-- x-ray
-- rheumatoid-arthritis
-- musculoskeletal
-- svdh
-- bone-segmentation
-- joint-localization
-- bone-erosion
-- jsn
-license: cc-by-4.0
----
-# RAM-H1200
-## Dataset Summary
-RAM-H1200 is a multi-task full-hand radiograph dataset for rheumatoid arthritis (RA) related image analysis. It is designed to support several clinically relevant computer vision tasks, including:
-- hand bone structure segmentation
-- bone erosion related segmentation
-- joint localization for Sharp/van der Heijde (SvdH) scoring
-- joint-level SvdH bone erosion (BE) scoring
-- joint-level SvdH joint space narrowing (JSN) scoring
-The dataset contains full-hand radiographs in BMP format, COCO-format annotations for segmentation and joint detection, joint-level ROI crops for scoring tasks, and study-level metadata.
-## Homepage
-- Dataset repository: `https://huggingface.co/datasets/TokyoTechMagicYang/RAM-H1200-v1`
-- Benchmark repository: `https://github.com/YSongxiao/RAM-H1200`
-## License
-This dataset is released under the **Creative Commons Attribution 4.0 International (CC BY 4.0)** license.
-## Supported Tasks and Applications
-RAM-H1200 supports the following research tasks:
-- **Segmentation**
-  - Bone segmentation on full-hand radiographs
-  - Bone erosion related segmentation
-- **Detection / Localization**
-  - Joint localization for BE-related regions
-  - Joint localization for JSN-related regions
-- **Classification / Scoring**
-  - Joint-level SvdH BE score prediction
-  - Joint-level SvdH JSN score prediction
-Potential use cases include:
-- automated RA severity assessment
-- multi-task medical image analysis
-- musculoskeletal structure segmentation
-- joint-level radiographic scoring
-- benchmarking AI systems for RA-related radiograph analysis
-## Dataset Structure
-```text
-RAM-H1200-v1/
-|-- Segmentation/
-|   |-- train/
-|   |   |-- JP_HMCRD_P0001_20210203_6791_L.bmp
-|   |   |-- JP_HMCRD_P0001_20210203_6791_R.bmp
-|   |   |-- ...
-|   |   |-- _annotations_bone_rle.coco.json
-|   |   |-- _annotations_be_rle.coco.json
-|   |-- val/
-|   |   |-- ...
-|   |   |-- _annotations_bone_rle.coco.json
-|   |   |-- _annotations_be_rle.coco.json
-|   |-- test/
-|   |   |-- ...
-|   |   |-- _annotations_bone_rle.coco.json
-|   |   |-- _annotations_be_rle.coco.json
-|-- SvdH_Scoring/
-|   |-- SvdH_BE_Scoring/
-|   |   |-- train/
-|   |   |   |-- JP_HMCRD_P0001_20210203_6791_L/
-|   |   |   |   |-- CMC-T.bmp
-|   |   |   |   |-- IP.bmp
-|   |   |   |   |-- L.bmp
-|   |   |   |   |-- MCP-I.bmp
-|   |   |   |   |-- ...
-|   |   |   |-- _annotations_be_joint_detection.coco.json
-|   |   |   |-- _annotation_be_scores.json
-|   |   |-- val/
-|   |   |   |-- ...
-|   |   |   |-- _annotations_be_joint_detection.coco.json
-|   |   |   |-- _annotation_be_scores.json
-|   |   |-- test/
-|   |   |   |-- ...
-|   |   |   |-- _annotations_be_joint_detection.coco.json
-|   |   |   |-- _annotation_be_scores.json
-|   |-- SvdH_JSN_Scoring/
-|   |   |-- train/
-|   |   |   |-- JP_HMCRD_P0001_20210203_6791_L/
-|   |   |   |   |-- CMC-M.bmp
-|   |   |   |   |-- CMC-R.bmp
-|   |   |   |   |-- CMC-S.bmp
-|   |   |   |   |-- MCP-I.bmp
-|   |   |   |   |-- ...
-|   |   |   |-- _annotations_jsn_joint_detection.coco.json
-|   |   |   |-- _annotation_jsn_scores.json
-|   |   |-- val/
-|   |   |   |-- ...
-|   |   |   |-- _annotations_jsn_joint_detection.coco.json
-|   |   |   |-- _annotation_jsn_scores.json
-|   |   |-- test/
-|   |   |   |-- ...
-|   |   |   |-- _annotations_jsn_joint_detection.coco.json
-|   |   |   |-- _annotation_jsn_scores.json
-|-- Metadata.xlsx
-```
-## Data Organization
-### 1. Segmentation
-The `Segmentation/` directory contains full-hand radiographs in BMP format, organized into `train`, `val`, and `test` splits.
-A typical filename looks like:
-```text
-JP_HMCRD_P0001_20210203_6791_L.bmp
-```
-This naming scheme generally encodes:
-- country or source prefix
-- acquisition center
-- anonymized patient identifier
-- study date (de-identified via a consistent temporal offset per patient)
-- image identifier
-- hand side (`L` for left, `R` for right)
-Each split contains two COCO-format annotation files:
-- `_annotations_bone_rle.coco.json`
-- `_annotations_be_rle.coco.json`
-#### Bone Segmentation Annotations
-`_annotations_bone_rle.coco.json` stores segmentation masks using COCO RLE encoding. The annotation categories include anatomical structures such as:
-- Capitate
-- Hamate
-- Lunate
-- Scaphoid
-- Trapezium
-- Trapezoid
-- Radius
-- Ulna
-- MC1--MC5
-- PP1--PP5
-- DP1--DP5
-The annotation file also contains some additional categories related to non-bony structures or acquisition artifacts, such as soft tissue or implants.
-Example COCO annotation:
-```json
-{
-  "id": 1,
-  "image_id": 0,
-  "category_id": 30,
-  "bbox": [14.0, 198.0, 852.0, 1233.0],
-  "area": 515212.0,
-  "segmentation": {
-    "size": [1431, 893],
-    "counts": "..."
-  }
-}
-```
-#### Bone Erosion Related Segmentation Annotations
-`_annotations_be_rle.coco.json` provides segmentation annotations related to bone erosion patterns. The category set includes:
-- `Fusion`
-- `Non-SvdH-BE`
-- `OP`
-- `SvdH-BE-50`
-- `SvdH-BE-90`
-These annotations are also stored in COCO RLE format.
-### 2. SvdH BE Scoring
-The `SvdH_Scoring/SvdH_BE_Scoring/` directory contains ROI crops for bone erosion scoring. Each case is stored in a separate folder named by a case identifier.
-Example:
-```text
-JP_HMCRD_P0001_20210203_6791_L/
-```
-A typical BE case folder contains 16 ROI images corresponding to joints or anatomical regions such as:
-- `CMC-T.bmp`
-- `IP.bmp`
-- `L.bmp`
-- `Tm.bmp`
-- `R.bmp`
-- `U.bmp`
-- `MCP-T.bmp`
-- `MCP-I.bmp`
-- `MCP-M.bmp`
-- `MCP-R.bmp`
-- `MCP-S.bmp`
-- `PIP-I.bmp`
-- `PIP-M.bmp`
-- `PIP-R.bmp`
-- `PIP-S.bmp`
-Each split also includes:
-- `_annotations_be_joint_detection.coco.json`
-- `_annotation_be_scores.json`
-#### BE Joint Detection
-`_annotations_be_joint_detection.coco.json` stores joint localization annotations in COCO format. The categories map to BE-relevant joints or regions, including:
-- `R`
-- `U`
-- `L`
-- `CMC-T`
-- `S`
-- `Tm`
-- `IP`
-- `MCP-T`
-- `MCP-I`
-- `MCP-M`
-- `MCP-R`
-- `MCP-S`
-- `PIP-I`
-- `PIP-M`
-- `PIP-R`
-- `PIP-S`
-#### BE Score Labels
-`_annotation_be_scores.json` stores ground-truth joint-level BE scores indexed by full image filename.
-Example:
-```json
-{
-  "JP_HMCRD_P0167_20111230_3497_L.bmp": {
-    "BE_MCP-T": 0,
-    "BE_MCP-I": 1,
-    "BE_MCP-M": 0,
-    "BE_MCP-R": 0,
-    "BE_MCP-S": 0,
-    "BE_IP": 0,
-    "BE_PIP-I": 0,
-    "BE_PIP-M": 0,
-    "BE_PIP-R": 1,
-    "BE_PIP-S": 1,
-    "BE_CMC-T": 0,
-    "BE_Tm": 1,
-    "BE_S": 0,
-    "BE_L": 0,
-    "BE_U": 0,
-    "BE_R": 0
-  }
-}
-```
-### 3. SvdH JSN Scoring
-The `SvdH_Scoring/SvdH_JSN_Scoring/` directory contains ROI crops for joint space narrowing scoring.
-A typical JSN case folder contains 15 ROI images corresponding to:
-- `CMC-M.bmp`
-- `CMC-R.bmp`
-- `CMC-S.bmp`
-- `SC.bmp`
-- `SR.bmp`
-- `STT.bmp`
-- `MCP-T.bmp`
-- `MCP-I.bmp`
-- `MCP-M.bmp`
-- `MCP-R.bmp`
-- `MCP-S.bmp`
-- `PIP-I.bmp`
-- `PIP-M.bmp`
-- `PIP-R.bmp`
-- `PIP-S.bmp`
-Each split also includes:
-- `_annotations_jsn_joint_detection.coco.json`
-- `_annotation_jsn_scores.json`
-#### JSN Joint Detection
-`_annotations_jsn_joint_detection.coco.json` stores COCO-format joint localization annotations. Categories include:
-- `CMC-M`
-- `CMC-R`
-- `CMC-S`
-- `SC`
-- `SR`
-- `STT`
-- `MCP-T`
-- `MCP-I`
-- `MCP-M`
-- `MCP-R`
-- `MCP-S`
-- `PIP-I`
-- `PIP-M`
-- `PIP-R`
-- `PIP-S`
-#### JSN Score Labels
-`_annotation_jsn_scores.json` stores ground-truth joint-level JSN scores indexed by full image filename.
-Example:
-```json
-{
-  "JP_HMCRD_P0167_20111230_3497_L.bmp": {
-    "JSN_MCP-T": 2,
-    "JSN_MCP-I": 0,
-    "JSN_MCP-M": 0,
-    "JSN_MCP-R": 0,
-    "JSN_MCP-S": 0,
-    "JSN_PIP-I": 0,
-    "JSN_PIP-M": 0,
-    "JSN_PIP-R": 0,
-    "JSN_PIP-S": 0,
-    "JSN_STT": 0,
-    "JSN_SC": 0,
-    "JSN_SR": 0,
-    "JSN_CMC-M": 0,
-    "JSN_CMC-R": 0,
-    "JSN_CMC-S": 0
-  }
-}
-```
-## Metadata
-The file `Metadata.xlsx` contains study-level metadata. Key columns include:
-- `Mapped Image Stem`
-- `StudyID`
-- `Normalized PatientID`
-- `isRA`
-- `Sex`
-- `Age`
-- `Center`
-- `PixelSpacing`
-- `ImageSize`
-- `LR`
-These fields provide normalized identifiers, demographic information, acquisition center information, study date, image geometry, and hand laterality.
-## Splits
-RAM-H1200 is distributed with predefined splits:
-- `train`
-- `val`
-- `test`
-These splits are consistently provided for:
-- segmentation
-- BE scoring
-- JSN scoring
-## Data Loading Notes
-This repository stores raw files rather than a single tabular annotation file. Depending on the task, users will typically load data as follows:
-- use BMP images together with the corresponding COCO JSON files for segmentation or detection tasks
-- use per-case ROI folders together with score JSON files for BE and JSN scoring tasks
-- use `Metadata.xlsx` for study-level metadata lookup and cohort analysis
-## Example Usage
-### Load COCO annotations
-```python
-import json
-from pathlib import Path
-ann_path = Path("Segmentation/train/_annotations_bone_rle.coco.json")
-with ann_path.open("r", encoding="utf-8") as f:
-    coco = json.load(f)
-print(len(coco["images"]))
-print(len(coco["annotations"]))
-print(coco["categories"][:5])
-```
-### Load BE score labels
-```python
-import json
-from pathlib import Path
-label_path = Path("SvdH_Scoring/SvdH_BE_Scoring/train/_annotation_be_scores.json")
-with label_path.open("r", encoding="utf-8") as f:
-    labels = json.load(f)
-sample_key = next(iter(labels))
-print(sample_key)
-print(labels[sample_key])
-```
-### Load JSN score labels
-```python
-import json
-from pathlib import Path
-label_path = Path("SvdH_Scoring/SvdH_JSN_Scoring/train/_annotation_jsn_scores.json")
-with label_path.open("r", encoding="utf-8") as f:
-    labels = json.load(f)
-sample_key = next(iter(labels))
-print(sample_key)
-print(labels[sample_key])
-```
-## Intended Uses
-RAM-H1200 is intended for research and benchmarking in:
-- rheumatoid arthritis radiograph analysis
-- automated scoring of structural damage
-- medical image segmentation
-- joint localization and ROI extraction
-- multi-task learning with hand radiographs
-## Out-of-Scope Uses
-This dataset is not intended for:
-- direct clinical deployment without independent validation
-- standalone medical decision-making
-- patient re-identification
-- non-research use without checking the dataset license and ethics approvals
-## Source Data
-RAM-H1200 consists of anonymized full-hand radiographs and derived annotations from multiple acquisition centers. It includes full-image labels, ROI-level labels, and metadata relevant to RA-related structural assessment.
-## Personal and Sensitive Information
-The dataset uses anonymized patient and study identifiers. Metadata is limited to research-relevant study and demographic information and does not include direct personal identifiers.
-## Bias, Risks, and Limitations
-- The dataset may reflect center-specific acquisition protocols and patient populations.
-- Annotation quality depends on the consistency of expert labeling and task definitions.
-- Some anatomical regions or score levels may be imbalanced.
-- Models trained on this dataset may not generalize to other institutions, scanners, or populations without additional validation.
-- The dataset is intended for research use, not for direct clinical diagnosis or treatment planning.
-## Citation
-If you use RAM-H1200 in your research, please cite the dataset and the associated paper.
-### BibTeX
-If there is an associated paper, add it here as well:
-```bibtex
-@article{ram_h1200_paper_2026,
-  title   = {<PAPER_TITLE_HERE>},
-  author  = {<AUTHOR_LIST>},
-  journal = {<JOURNAL_OR_CONFERENCE_HERE>},
-  year    = {2026},
-  url     = {<PAPER_URL_HERE>}
-}
-```
-## Acknowledgements
-We thank the annotators, clinicians, and collaborating institutions who contributed to the collection, curation, and quality control of RAM-H1200.
-## Contact
-For questions, issues, or collaboration inquiries, please contact:
-- `Songxiao Yang, Yafei Ou`
-- `syang(at)ok.sc.e.titech.ac.jp, yafei.ou(at)riken.jp`
 - `https://yafeiou.github.io/RAM10K`

+---
+language:
+- en
+pretty_name: RAM-H1200
+size_categories:
+- 1K<n<10K
+task_categories:
+- image-segmentation
+# - object-detection
+- image-classification
+task_ids:
+- semantic-segmentation
+- instance-segmentation
+- object-detection
+- multi-class-classification
+tags:
+- medical
+- radiography
+- x-ray
+- rheumatoid-arthritis
+- musculoskeletal
+- svdh
+- bone-segmentation
+- joint-localization
+- bone-erosion
+- jsn
+license: cc-by-4.0
+---
+# RAM-H1200
+## Dataset Summary
+RAM-H1200 is a multi-task full-hand radiograph dataset for rheumatoid arthritis (RA) related image analysis. It is designed to support several clinically relevant computer vision tasks, including:
+- hand bone structure segmentation
+- bone erosion related segmentation
+- joint localization for Sharp/van der Heijde (SvdH) scoring
+- joint-level SvdH bone erosion (BE) scoring
+- joint-level SvdH joint space narrowing (JSN) scoring
+The dataset contains full-hand radiographs in BMP format, COCO-format annotations for segmentation and joint detection, joint-level ROI crops for scoring tasks, and study-level metadata.
+## Homepage
+- Dataset repository: `https://huggingface.co/datasets/TokyoTechMagicYang/RAM-H1200-v1`
+- Benchmark repository: `https://github.com/YSongxiao/RAM-H1200`
+## License
+This dataset is released under the **Creative Commons Attribution 4.0 International (CC BY 4.0)** license.
+## Supported Tasks and Applications
+RAM-H1200 supports the following research tasks:
+- **Segmentation**
+  - Bone segmentation on full-hand radiographs
+  - Bone erosion related segmentation
+- **Detection / Localization**
+  - Joint localization for BE-related regions
+  - Joint localization for JSN-related regions
+- **Classification / Scoring**
+  - Joint-level SvdH BE score prediction
+  - Joint-level SvdH JSN score prediction
+Potential use cases include:
+- automated RA severity assessment
+- multi-task medical image analysis
+- musculoskeletal structure segmentation
+- joint-level radiographic scoring
+- benchmarking AI systems for RA-related radiograph analysis
+## Dataset Structure
+```text
+RAM-H1200-v1/
+|-- Segmentation/
+|   |-- train/
+|   |   |-- JP_HMCRD_P0001_20210203_6791_L.bmp
+|   |   |-- JP_HMCRD_P0001_20210203_6791_R.bmp
+|   |   |-- ...
+|   |   |-- _annotations_bone_rle.coco.json
+|   |   |-- _annotations_be_rle.coco.json
+|   |-- val/
+|   |   |-- ...
+|   |   |-- _annotations_bone_rle.coco.json
+|   |   |-- _annotations_be_rle.coco.json
+|   |-- test/
+|   |   |-- ...
+|   |   |-- _annotations_bone_rle.coco.json
+|   |   |-- _annotations_be_rle.coco.json
+|-- SvdH_Scoring/
+|   |-- SvdH_BE_Scoring/
+|   |   |-- train/
+|   |   |   |-- JP_HMCRD_P0001_20210203_6791_L/
+|   |   |   |   |-- CMC-T.bmp
+|   |   |   |   |-- IP.bmp
+|   |   |   |   |-- L.bmp
+|   |   |   |   |-- MCP-I.bmp
+|   |   |   |   |-- ...
+|   |   |   |-- _annotations_be_joint_detection.coco.json
+|   |   |   |-- _annotation_be_scores.json
+|   |   |-- val/
+|   |   |   |-- ...
+|   |   |   |-- _annotations_be_joint_detection.coco.json
+|   |   |   |-- _annotation_be_scores.json
+|   |   |-- test/
+|   |   |   |-- ...
+|   |   |   |-- _annotations_be_joint_detection.coco.json
+|   |   |   |-- _annotation_be_scores.json
+|   |-- SvdH_JSN_Scoring/
+|   |   |-- train/
+|   |   |   |-- JP_HMCRD_P0001_20210203_6791_L/
+|   |   |   |   |-- CMC-M.bmp
+|   |   |   |   |-- CMC-R.bmp
+|   |   |   |   |-- CMC-S.bmp
+|   |   |   |   |-- MCP-I.bmp
+|   |   |   |   |-- ...
+|   |   |   |-- _annotations_jsn_joint_detection.coco.json
+|   |   |   |-- _annotation_jsn_scores.json
+|   |   |-- val/
+|   |   |   |-- ...
+|   |   |   |-- _annotations_jsn_joint_detection.coco.json
+|   |   |   |-- _annotation_jsn_scores.json
+|   |   |-- test/
+|   |   |   |-- ...
+|   |   |   |-- _annotations_jsn_joint_detection.coco.json
+|   |   |   |-- _annotation_jsn_scores.json
+|-- Metadata.xlsx
+```
+## Data Organization
+### 1. Segmentation
+The `Segmentation/` directory contains full-hand radiographs in BMP format, organized into `train`, `val`, and `test` splits.
+A typical filename looks like:
+```text
+JP_HMCRD_P0001_20210203_6791_L.bmp
+```
+This naming scheme generally encodes:
+- country or source prefix
+- acquisition center
+- anonymized patient identifier
+- study date (de-identified via a consistent temporal offset per patient)
+- image identifier
+- hand side (`L` for left, `R` for right)
+Each split contains two COCO-format annotation files:
+- `_annotations_bone_rle.coco.json`
+- `_annotations_be_rle.coco.json`
+#### Bone Segmentation Annotations
+`_annotations_bone_rle.coco.json` stores segmentation masks using COCO RLE encoding. The annotation categories include anatomical structures such as:
+- Capitate
+- Hamate
+- Lunate
+- Scaphoid
+- Trapezium
+- Trapezoid
+- Radius
+- Ulna
+- MC1--MC5
+- PP1--PP5
+- DP1--DP5
+The annotation file also contains some additional categories related to non-bony structures or acquisition artifacts, such as soft tissue or implants.
+Example COCO annotation:
+```json
+{
+  "id": 1,
+  "image_id": 0,
+  "category_id": 30,
+  "bbox": [14.0, 198.0, 852.0, 1233.0],
+  "area": 515212.0,
+  "segmentation": {
+    "size": [1431, 893],
+    "counts": "..."
+  }
+}
+```
+#### Bone Erosion Related Segmentation Annotations
+`_annotations_be_rle.coco.json` provides segmentation annotations related to bone erosion patterns. The category set includes:
+- `Non-SvdH-BE`
+- `SvdH-BE-50`
+- `SvdH-BE-90`
+These annotations are also stored in COCO RLE format.
+### 2. SvdH BE Scoring
+The `SvdH_Scoring/SvdH_BE_Scoring/` directory contains ROI crops for bone erosion scoring. Each case is stored in a separate folder named by a case identifier.
+Example:
+```text
+JP_HMCRD_P0001_20210203_6791_L/
+```
+A typical BE case folder contains 16 ROI images corresponding to joints or anatomical regions such as:
+- `CMC-T.bmp`
+- `IP.bmp`
+- `L.bmp`
+- `Tm.bmp`
+- `R.bmp`
+- `U.bmp`
+- `MCP-T.bmp`
+- `MCP-I.bmp`
+- `MCP-M.bmp`
+- `MCP-R.bmp`
+- `MCP-S.bmp`
+- `PIP-I.bmp`
+- `PIP-M.bmp`
+- `PIP-R.bmp`
+- `PIP-S.bmp`
+Each split also includes:
+- `_annotations_be_joint_detection.coco.json`
+- `_annotation_be_scores.json`
+#### BE Joint Detection
+`_annotations_be_joint_detection.coco.json` stores joint localization annotations in COCO format. The categories map to BE-relevant joints or regions, including:
+- `R`
+- `U`
+- `L`
+- `CMC-T`
+- `S`
+- `Tm`
+- `IP`
+- `MCP-T`
+- `MCP-I`
+- `MCP-M`
+- `MCP-R`
+- `MCP-S`
+- `PIP-I`
+- `PIP-M`
+- `PIP-R`
+- `PIP-S`
+#### BE Score Labels
+`_annotation_be_scores.json` stores ground-truth joint-level BE scores indexed by full image filename.
+Example:
+```json
+{
+  "JP_HMCRD_P0167_20111230_3497_L.bmp": {
+    "BE_MCP-T": 0,
+    "BE_MCP-I": 1,
+    "BE_MCP-M": 0,
+    "BE_MCP-R": 0,
+    "BE_MCP-S": 0,
+    "BE_IP": 0,
+    "BE_PIP-I": 0,
+    "BE_PIP-M": 0,
+    "BE_PIP-R": 1,
+    "BE_PIP-S": 1,
+    "BE_CMC-T": 0,
+    "BE_Tm": 1,
+    "BE_S": 0,
+    "BE_L": 0,
+    "BE_U": 0,
+    "BE_R": 0
+  }
+}
+```
+### 3. SvdH JSN Scoring
+The `SvdH_Scoring/SvdH_JSN_Scoring/` directory contains ROI crops for joint space narrowing scoring.
+A typical JSN case folder contains 15 ROI images corresponding to:
+- `CMC-M.bmp`
+- `CMC-R.bmp`
+- `CMC-S.bmp`
+- `SC.bmp`
+- `SR.bmp`
+- `STT.bmp`
+- `MCP-T.bmp`
+- `MCP-I.bmp`
+- `MCP-M.bmp`
+- `MCP-R.bmp`
+- `MCP-S.bmp`
+- `PIP-I.bmp`
+- `PIP-M.bmp`
+- `PIP-R.bmp`
+- `PIP-S.bmp`
+Each split also includes:
+- `_annotations_jsn_joint_detection.coco.json`
+- `_annotation_jsn_scores.json`
+#### JSN Joint Detection
+`_annotations_jsn_joint_detection.coco.json` stores COCO-format joint localization annotations. Categories include:
+- `CMC-M`
+- `CMC-R`
+- `CMC-S`
+- `SC`
+- `SR`
+- `STT`
+- `MCP-T`
+- `MCP-I`
+- `MCP-M`
+- `MCP-R`
+- `MCP-S`
+- `PIP-I`
+- `PIP-M`
+- `PIP-R`
+- `PIP-S`
+#### JSN Score Labels
+`_annotation_jsn_scores.json` stores ground-truth joint-level JSN scores indexed by full image filename.
+Example:
+```json
+{
+  "JP_HMCRD_P0167_20111230_3497_L.bmp": {
+    "JSN_MCP-T": 2,
+    "JSN_MCP-I": 0,
+    "JSN_MCP-M": 0,
+    "JSN_MCP-R": 0,
+    "JSN_MCP-S": 0,
+    "JSN_PIP-I": 0,
+    "JSN_PIP-M": 0,
+    "JSN_PIP-R": 0,
+    "JSN_PIP-S": 0,
+    "JSN_STT": 0,
+    "JSN_SC": 0,
+    "JSN_SR": 0,
+    "JSN_CMC-M": 0,
+    "JSN_CMC-R": 0,
+    "JSN_CMC-S": 0
+  }
+}
+```
+## Metadata
+The file `Metadata.xlsx` contains study-level metadata. Key columns include:
+- `Mapped Image Stem`
+- `StudyID`
+- `Normalized PatientID`
+- `isRA`
+- `Sex`
+- `Age`
+- `Center`
+- `PixelSpacing`
+- `ImageSize`
+- `LR`
+These fields provide normalized identifiers, demographic information, acquisition center information, study date, image geometry, and hand laterality.
+## Splits
+RAM-H1200 is distributed with predefined splits:
+- `train`
+- `val`
+- `test`
+These splits are consistently provided for:
+- segmentation
+- BE scoring
+- JSN scoring
+## Data Loading Notes
+This repository stores raw files rather than a single tabular annotation file. Depending on the task, users will typically load data as follows:
+- use BMP images together with the corresponding COCO JSON files for segmentation or detection tasks
+- use per-case ROI folders together with score JSON files for BE and JSN scoring tasks
+- use `Metadata.xlsx` for study-level metadata lookup and cohort analysis
+## Example Usage
+### Load COCO annotations
+```python
+import json
+from pathlib import Path
+ann_path = Path("Segmentation/train/_annotations_bone_rle.coco.json")
+with ann_path.open("r", encoding="utf-8") as f:
+    coco = json.load(f)
+print(len(coco["images"]))
+print(len(coco["annotations"]))
+print(coco["categories"][:5])
+```
+### Load BE score labels
+```python
+import json
+from pathlib import Path
+label_path = Path("SvdH_Scoring/SvdH_BE_Scoring/train/_annotation_be_scores.json")
+with label_path.open("r", encoding="utf-8") as f:
+    labels = json.load(f)
+sample_key = next(iter(labels))
+print(sample_key)
+print(labels[sample_key])
+```
+### Load JSN score labels
+```python
+import json
+from pathlib import Path
+label_path = Path("SvdH_Scoring/SvdH_JSN_Scoring/train/_annotation_jsn_scores.json")
+with label_path.open("r", encoding="utf-8") as f:
+    labels = json.load(f)
+sample_key = next(iter(labels))
+print(sample_key)
+print(labels[sample_key])
+```
+## Intended Uses
+RAM-H1200 is intended for research and benchmarking in:
+- rheumatoid arthritis radiograph analysis
+- automated scoring of structural damage
+- medical image segmentation
+- joint localization and ROI extraction
+- multi-task learning with hand radiographs
+## Out-of-Scope Uses
+This dataset is not intended for:
+- direct clinical deployment without independent validation
+- standalone medical decision-making
+- patient re-identification
+- non-research use without checking the dataset license and ethics approvals
+## Source Data
+RAM-H1200 consists of anonymized full-hand radiographs and derived annotations from multiple acquisition centers. It includes full-image labels, ROI-level labels, and metadata relevant to RA-related structural assessment.
+## Personal and Sensitive Information
+The dataset uses anonymized patient and study identifiers. Metadata is limited to research-relevant study and demographic information and does not include direct personal identifiers.
+## Bias, Risks, and Limitations
+- The dataset may reflect center-specific acquisition protocols and patient populations.
+- Annotation quality depends on the consistency of expert labeling and task definitions.
+- Some anatomical regions or score levels may be imbalanced.
+- Models trained on this dataset may not generalize to other institutions, scanners, or populations without additional validation.
+- The dataset is intended for research use, not for direct clinical diagnosis or treatment planning.
+## Citation
+If you use RAM-H1200 in your research, please cite the dataset and the associated paper.
+### BibTeX
+If there is an associated paper, add it here as well:
+```bibtex
+@article{ram_h1200_paper_2026,
+  title   = {RAM-H1200: A Unified Evaluation and Dataset on Hand Radiographs for Rheumatoid Arthritis},
+  author  = {Songxiao Yang, Haolin Wang, Yao Fu, Junmu Peng, Lin Fan, Hongruixuan Chen, Jian Song, Masayuki Ikebe, Shinya Takamaeda-Yamazaki, Masatoshi Okutomi, Tamotsu Kamishima, Yafei Ou},
+  journal = {<JOURNAL_OR_CONFERENCE_HERE>},
+  year    = {2026},
+  url     = {<PAPER_URL_HERE>}
+}
+```
+## Acknowledgements
+We thank the annotators, clinicians, and collaborating institutions who contributed to the collection, curation, and quality control of RAM-H1200.
+## Contact
+For questions, issues, or collaboration inquiries, please contact:
+- `Songxiao Yang, Yafei Ou`
+- `syang(at)ok.sc.e.titech.ac.jp, yafei.ou(at)riken.jp`
 - `https://yafeiou.github.io/RAM10K`