YongchengYAO/MedVision

MedVision: Dataset and Benchmark for Quantitative Medical Image Analysis

| 🌏 Project | 🧑🏻‍💻 Code | 🩻 Dataset | 🐳 Docker | 🤗 Models | 📖 arXiv |

🔎 Benchmarking VLMs for detection, tumor/lesion size estimation, and angle/distance measurement from medical images 📏

💿 30.8M annotated samples | multi-modality | multi-anatomy | 3D/2D medical image 💿

@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}, 
}

News

• [Oct 8, 2025] 🚀 Release MedVision dataset v1.0.0

Change Log

For essential updates, check the change log.

Datasets

File structure: raw data will be automatically downloaded and processed, and our annotations are in each dataset folder

📝 The MedVision dataset consists of public medical images and quantitative annotations from this study. MRI: Magnetic Resonance Imaging; CT: Computed Tomography; PET: positron emission tomography; US: Ultrasound; b-box: bounding box; T/L: tumor/lesion size; A/D: angle/distance; HF: HuggingFace; GC: Grand-Challenge; * redistributed.

Dataset	Anatomy	Modality	Annotation	Availability	Source	# Sample (Train/Test)			Status
						b-box	T/L	A/D
AbdomenAtlas	abdomen	CT	b-box	open	HF	6.8 / 2.9M	0	0	✅
AbdomenCT-1K	abdomen	CT	b-box	open	Zenodo	0.7 / 0.3M	0	0	✅
ACDC	heart	MRI	b-box	open	HF*, others	9.5 / 4.8K	0	0	✅
AMOS22	abdomen	CT, MRI	b-box	open	Zenodo	0.8 / 0.3M	0	0	✅
autoPEI-III	whole body	CT, PET	b-box, T/L	open	HF*, others	22 / 9.7K	0.5 / 0.2K	0	✅
BCV15	abdomen	CT	b-box	open	HF*, Synapse	71 / 30K	0	0	✅
BraTS24	brain	MRI	b-box, T/L	open	HF*, Synapse	0.8 / 0.3M	7.9 / 3.1K	0	✅
CAMUS	heart	US	b-box	open	HF*, others	0.7 / 0.3M	0	0	✅
Ceph-Bio-400	head and neck	X-ray	b-box, A/D	open	HF*, others	0	0	5.3 / 2.3K	✅
CrossModDA	brain	MRI	b-box	open	HF*, Zenodo	3.0 / 1.0K	0	0	✅
FeTA24	fetal brain	MRI	b-box, A/D	registration	Synapse	34 / 15K	0	0.2 / 0.1K	✅
FLARE22	abdomen	CT	b-box	open	HF*, others	72 / 33K	0	0	✅
HNTSMRG24	head and neck	MRI	b-box, T/L	open	Zenodo	18 / 6.6K	1.0 / 0.4K	0	✅
ISLES24	brain	MRI	b-box	open	HF*, GC	7.3 / 2.5K	0	0	✅
KiPA22	kidney	CT	b-box, T/L	open	HF*, GC	26 / 11K	2.1 / 1.0K	0	✅
KiTS23	kidney	CT	b-box, T/L	open	HF*, GC	80 / 35K	5.9 / 2.6K	0	✅
MSD	multiple	CT, MRI	b-box, T/L	open	others	0.2 / 0.1M	5.3 / 2.2K	0	✅
OAIZIB-CM	knee	MRI	b-box	open	HF	0.5 / 0.2M	0	0	✅
SKM-TEA	knee	MRI	b-box	registration	others	0.2 / 0.1M	0	0	✅
ToothFairy2	tooth	CT	b-box	registration	others	1.0 / 0.4M	0	0	✅
TopCoW24	brain	CT, MRI	b-box	open	HF*, Zenodo	43 / 20K	0	0	✅
TotalSegmentator	multiple	CT, MRI	b-box	open	HF*, Zenodo	9.6 / 4.0M	0	0	✅
Total						22 / 9.2M	23 / 9.6K	5.6 / 2.4K

⚠️ For the following datasets, which do not allow redistribution, you need to apply for access from data owners, (optionally) upload to your private HF dataset repo, and set corresponding environment variables.

Dataset	Source	Host Platform	Env Var
FeTA24	https://www.synapse.org/Synapse:syn25649159/wiki/610007	Synapse	SYNAPSE_TOKEN
SKM-TEA	https://aimi.stanford.edu/datasets/skm-tea-knee-mri	Huggingface	MedVision_SKMTEA_HF_ID
ToothFairy2	https://ditto.ing.unimore.it/toothfairy2/	Huggingface	MedVision_ToothFairy2_HF_ID

📝 For SKM-TEA and ToothFairy2, you need to process the raw data and upload the preprocessed data to your private HF dataset repo. To use HF private dataset, you need to set HF_TOKEN and login with hf auth login --token $HF_TOKEN --add-to-git-credential

• Prepare SKM-TEA data: tutorial
• Prepare ToothFairy2 data: tutorial

New Datasets Guide

To add new datasets, check this blog for an introduction of MedVision dataset.

Requirement

📝 Note: trust_remote_code is no longer supported in datasets>=4.0.0, install dataset with pip install datasets==3.6.0

Use

import os
from datasets import load_dataset

# Set data folder
os.environ["MedVision_DATA_DIR"] = <your/data/folder>

# Pick a dataset config name and split
config = <config-name> # e.g., "OAIZIB-CM_BoxSize_Task01_Axial_Test"
split_name = "test" # use "test" for testing set config; use "train" for training set config 

# Get dataset
ds = load_dataset(
        "YongchengYAO/MedVision",
        name=config,
        trust_remote_code=True,
        split=split_name,
    )

📝 List of config names here (./info)

Environment Variables

# Set where data will be saved, requires ~1T for the complete dataset
export MedVision_DATA_DIR=<your/data/folder>

# Force download and process raw images, default to "False"
export MedVision_FORCE_DOWNLOAD_DATA="False"

# Force install dataset codebase, default to "False"
export MedVision_FORCE_INSTALL_CODE="False"

📖 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
    • 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
      • 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 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.

Code block in `MedVision(GeneratorBasedBuilder)` (Click to expand)

"""
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"),
        },
    ),
}

Code block in `_generate_examples` (Click to expand)

# 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"],
                }

Dataset Building Workflow

Workflow

MedVision Dataset Building Workflow (Black)

MedVision Dataset Building Workflow (White)

There are a few venues to control the dataset loading and building behavior:

• Rebuild Dataset (Arrow files): Use the download_mode argument in load_dataset() (docs).
  • [1] Set download_mode="force_redownload" to ignore the cached Arrow files and trigger the data loading script MedVision.py to rebuild the dataset.
• Redownload Raw Data:
  • [2] MedVision_FORCE_DOWNLOAD_DATA: Set this environment variable to True to force re-downloading raw images and annotations.
  • [3] .downloaded_datasets.json: This tracker file records downloaded status. Removing a dataset's entry here will trigger a re-download of the raw data for that dataset.

⚠️ How to properly update/redownload raw data?

If you need to update raw data (images, masks, landmarks) using [2] or [3], you MUST ALSO use [1] (download_mode="force_redownload").

Why? Because if Hugging Face finds a valid cached dataset (Arrow files), it will load it directly and skip running the script entirely. Without running the script, the environment variable [2] or tracker file [3] will never be checked.

Summary:

• Update Arrow/Fields only: Use [1].
• Update Raw Data: Use [1] AND ([2] or [3]).

🔥 We will maintain a change log for essential updates.

Examples

Run this for the first time will download the raw data and build the dataset

import os
from datasets import load_dataset

# Set data folder
wd = os.path.join(os.getcwd(), "Data-testing")
os.makedirs(wd, exist_ok=True)
os.environ["MedVision_DATA_DIR"] = wd

# Pick a dataset config name and split
config = "OAIZIB-CM_BoxSize_Task01_Axial_Test"
split_name = "test" # use "test" for testing set config; use "train" for training set config 

# Get dataset
ds = load_dataset(
        "YongchengYAO/MedVision",
        name=config,
        trust_remote_code=True,
        split=split_name,
    )

Run the same script again will use the cached dataset

import os
from datasets import load_dataset

# Set data folder
wd = os.path.join(os.getcwd(), "Data-testing")
os.makedirs(wd, exist_ok=True)
os.environ["MedVision_DATA_DIR"] = wd

# Pick a dataset config name and split
config = "OAIZIB-CM_BoxSize_Task01_Axial_Test"
split_name = "test" # use "test" for testing set config; use "train" for training set config 

# Get dataset
ds = load_dataset(
        "YongchengYAO/MedVision",
        name=config,
        trust_remote_code=True,
        split=split_name,
    )

Adding `download_mode="force_redownload"` will skip raw data downloading and rebuild the dataset

import os
from datasets import load_dataset

# Set data folder
wd = os.path.join(os.getcwd(), "Data-testing")
os.makedirs(wd, exist_ok=True)
os.environ["MedVision_DATA_DIR"] = wd

# Pick a dataset config name and split
config = "OAIZIB-CM_BoxSize_Task01_Axial_Test"
split_name = "test" # use "test" for testing set config; use "train" for training set config 

# Get dataset
ds = load_dataset(
        "YongchengYAO/MedVision",
        name=config,
        trust_remote_code=True,
        split=split_name,
        download_mode="force_redownload",
    )

Adding `download_mode="force_redownload"` and `os.environ["MedVision_FORCE_DOWNLOAD_DATA"] = "True"` will redownload raw data and rebuild the dataset

import os
from datasets import load_dataset

# Set data folder
wd = os.path.join(os.getcwd(), "Data-testing")
os.makedirs(wd, exist_ok=True)
os.environ["MedVision_DATA_DIR"] = wd

# Pick a dataset config name and split
config = "OAIZIB-CM_BoxSize_Task01_Axial_Test"
split_name = "test" # use "test" for testing set config; use "train" for training set config 

# Force redownload
os.environ["MedVision_FORCE_DOWNLOAD_DATA"] = "True"

# Get dataset
ds = load_dataset(
        "YongchengYAO/MedVision",
        name=config,
        trust_remote_code=True,
        split=split_name,
        download_mode="force_redownload",
    )

Download Mode in MedVision Dataset

(Advanced) Understand how the customized dataset loading script `MedVision.py` changes the behavior of `download_mode` in `load_dataset()`

• download_mode can be one of these: "reuse_dataset_if_exists" (default), "reuse_cache_if_exists", "force_redownload"

• Default behavior of download_mode in load_dataset():

  	Downloads	Dataset
  reuse_dataset_if_exists (default)	Reuse	Reuse
  reuse_cache_if_exists	Reuse	Fresh
  force_redownload	Fresh	Fresh

• download_mode in MedVision dataset:

  	Downloads	Dataset
  reuse_dataset_if_exists (default)	Reuse	Reuse
  reuse_cache_if_exists	Reuse	Fresh
  force_redownload (MedVision_FORCE_DOWNLOAD_DATA=False)	Reuse	Fresh
  force_redownload (MedVision_FORCE_DOWNLOAD_DATA=True)	Fresh	Fresh

Advanced Usage

The dataset codebase medvision_ds can be used to scale the dataset, including adding new annotation types and datasets.

🛠️ Install

pip install "git+https://huggingface.co/datasets/YongchengYAO/MedVision.git#subdirectory=src"
pip show medvision_ds

or

# First, install the benchmark codebase: medvision_bm
pip install "git+https://github.com/YongchengYAO/MedVision.git" 
 
# Install the dataset codebase: medvision_ds
python -m medvision_bm.benchmark.install_medvision_ds --data_dir <local-data-folder>  

🧑🏻‍💻 Use utility functions for image processing

from medvision_ds.utils.data_conversion import (
    convert_nrrd_to_nifti,
    convert_mha_to_nifti,
    convert_nii_to_niigz,
    convert_bmp_to_niigz,
    copy_img_header_to_mask,
    reorient_niigz_RASplus_batch_inplace,
)

from medvision_ds.utils.preprocess_utils import (
    split_4d_nifti,
) 

👩🏼‍💻Examples of dataset scaling:

• Setup automatic data processing pipeline

  • Download preprocessed data from HF: medvision_ds/datasets/OAIZIB_CM/download.py
  • Download and processed data from source: medvision_ds/datasets/BraTS24/download_raw.py

• Prepare annotations

  • Generate b-box annotations from segmentation masks:

    • medvision_ds/datasets/BraTS24/preprocess_detection.py

  • Generate tumor/lesion size (TL) annotations from segmentation masks:

    • medvision_ds/datasets/BraTS24/preprocess_biometry.py

  • Generate angle/distance (AD) annotations from landmarks:

    • medvision_ds/datasets/Ceph_Biometrics_400/preprocess_biometry.py
    • medvision_ds/datasets/FeTA24/preprocess_biometry.py

License: CC-BY-NC-4.0

This repository is released under CC-BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).

✅ Do

• Copy and redistribute the material in any medium or format.
• Adapt, remix, transform, and build upon the material.
• Use it privately or in non-commercial educational, research, or personal projects.

🚫 Do not

• Use the material for commercial purposes

📄 Requirement

Requirement	Description
Attribution (BY)	You must give appropriate credit, provide a link to this dataset, and indicate if changes were made.
NonCommercial (NC)	You may not use the material for commercial purposes.
Indicate changes	If you modify the work, you must note that it has been changed.