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Co-authored-by: Elena Sizikova <evsizikova@users.noreply.huggingface.co>

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+ *.7z filter=lfs diff=lfs merge=lfs -text
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+ *.safetensors filter=lfs diff=lfs merge=lfs -text
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+ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+ *.tar.* filter=lfs diff=lfs merge=lfs -text
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+ *.zst filter=lfs diff=lfs merge=lfs -text
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+ *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ # Audio files - uncompressed
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+ *.pcm filter=lfs diff=lfs merge=lfs -text
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+ *.sam filter=lfs diff=lfs merge=lfs -text
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+ # Image files - compressed
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+ ---
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+ license: cc0-1.0
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+ task_categories:
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+ - image-classification
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+ - image-segmentation
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+ tags:
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+ - medical
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+ pretty_name: M-SYNTH
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+ size_categories:
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+ - 10K<n<100K
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+ ---
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+ # M-SYNTH
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+
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+ <!-- Provide a quick summary of the dataset. -->
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+
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+ M-SYNTH is a synthetic digital mammography (DM) dataset with four breast fibroglandular density distributions imaged using Monte Carlo x-ray simulations with the publicly available [Virtual Imaging Clinical Trial for Regulatory Evaluation (VICTRE)](https://github.com/DIDSR/VICTRE) toolkit.
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+
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+ ## Dataset Details
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+
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+ The dataset has the following characteristics:
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+
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+ * Breast density: dense, heterogeneously dense, scattered, fatty
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+ * Mass radius (mm): 5.00, 7.00, 9.00
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+ * Mass density: 1.0, 1.06, 1.1 (ratio of radiodensity of the mass to that of fibroglandular tissue)
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+ * Relative dose: 20%, 40%, 60%, 80%, 100% of the clinically recommended dose for each density
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+
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+ <p align="center">
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+ <img src='https://raw.githubusercontent.com/DIDSR/msynth-release/main/images/examples.png' width='700'>
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+ </p>
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+
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+ ### Dataset Description
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+
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+ <!-- Provide a longer summary of what this dataset is. -->
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+
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+ - **Curated by:** [Elena Sizikova](https://esizikova.github.io/), [Niloufar Saharkhiz](https://www.linkedin.com/in/niloufar-saharkhiz/), [Diksha Sharma](https://www.linkedin.com/in/diksha-sharma-6059977/), [Miguel Lago](https://www.linkedin.com/in/milaan/), [Berkman Sahiner](https://www.linkedin.com/in/berkman-sahiner-6aa9a919/), [Jana Gut Delfino](https://www.linkedin.com/in/janadelfino/), [Aldo Badano](https://www.linkedin.com/in/aldobadano/)
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+ - **License:** Creative Commons 1.0 Universal License (CC0)
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+
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+ ### Dataset Sources
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+
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+ <!-- Provide the basic links for the dataset. -->
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+
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+ - **Code:** [https://github.com/DIDSR/msynth-release](https://github.com/DIDSR/msynth-release)
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+ - **Paper:** [https://arxiv.org/pdf/2310.18494.pdf](https://arxiv.org/pdf/2310.18494.pdf)
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+ - **Demo:** [https://github.com/DIDSR/msynth-release/tree/master/examples](https://github.com/DIDSR/msynth-release/tree/master/examples)
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+
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+ ## Uses
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+
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+ <!-- Address questions around how the dataset is intended to be used. -->
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+
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+ M-SYNTH is intended to facilitate testing of AI with pre-computed synthetic mammography data.
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+
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+ ### Direct Use
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+
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+ <!-- This section describes suitable use cases for the dataset. -->
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+
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+ M-SYNTH can be used to evaluate the effect of mass size and density, breast density, and dose on AI performance in lesion detection.
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+ M-SYNTH can be used to either train or test pre-trained AI models.
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+
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+ ### Out-of-Scope Use
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+
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+ <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
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+
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+ M-SYNTH cannot be used in lieu of real patient examples to make performance determinations.
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+
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+
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+ ## Dataset Structure
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+
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+ <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
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+
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+ M-SYNTH is organized into a directory structure that indicates the parameters. The folder
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+ ```
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+ device_data_VICTREPhantoms_spic_[LESION_DENSITY]/[DOSE]/[BREAST_DENSITY]/2/[LESION_SIZE]/SIM/P2_[LESION_SIZE]_[BREAST_DENSITY].8337609.[PHANTOM_FILE_ID]/[PHANTOM_FILEID]/
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+ ```
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+ contains image files imaged with the specified parameters. Note that only examples with odd PHANTOM_FILEID contain lesions, others do not.
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+
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+
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+ ```
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+ $ tree data/device_data_VICTREPhantoms_spic_1.0/1.02e10/hetero/2/5.0/SIM/P2_5.0_hetero.8337609.1/1/
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+
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+ data/device_data_VICTREPhantoms_spic_1.0/1.02e10/hetero/2/5.0/SIM/P2_5.0_hetero.8337609.1/1/
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+ ├── DICOM_dm
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+ │   └── 000.dcm
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+ ├── projection_DM1.loc
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+ ├── projection_DM1.mhd
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+ └── projection_DM1.raw
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+ ```
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+ Each folder contains mammogram data that can be read from .raw format (.mhd contains supporting data), or DICOM (.dcm) format.
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+ Coordinates of lesions can be found in .loc files. Segmentations are stored in .raw format and can be found in data/segmentation_masks/* .
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+
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+ See [Github](https://github.com/DIDSR/msynth-release/tree/main/code) for examples of how to access the files, and [examples](https://github.com/DIDSR/msynth-release/tree/main/examples) for code to load each type of file.
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+ ## Bias, Risks, and Limitations
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+
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+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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+
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+ Simulation-based testing is constrained to the parameter variability represented in the object model and the acquisition system.
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+ There is a risk of misjudging model performance if the simulated examples do not capture the variability in real patients. Please
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+ see the paper for a full discussion of biases, risks, and limitations.
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+
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+ ## How to use it
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+ The msynth dataset is a very large dataset so for most use cases it is recommended to make use of the streaming API of `datasets`.
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+ The msynth dataset has three configurations: 1) device_data, 2) segmentation_mask, and 3) metadata
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+ You can load and iterate through the dataset using the configurations with the following lines of code:
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+
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+ ```python
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+ from datasets import load_dataset
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+ ds = load_dataset("didsr/msynth", 'device_data') # For device data for all breast density, mass redius, mass density, and relative dose, change configuration to 'segmentation_mask' and 'metadata' to load the segmentation masks and bound information
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+ print(ds_data["device_data"])
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+
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+ # A sample data instance
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+
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+ {'Raw': '~\\.cache\\huggingface\\datasets\\downloads\\extracted\\59384cf05fc44e8c0cb23bb19e1fcd8f0c39720b282109d204a85561fe66bdb1\\SIM\\P2_5.0_fatty.8336179.1\\1\\projection_DM1.raw',
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+ 'mhd': '~/.cache/huggingface/datasets/downloads/extracted/59384cf05fc44e8c0cb23bb19e1fcd8f0c39720b282109d204a85561fe66bdb1/SIM/P2_5.0_fatty.8336179.1/1\\projection_DM1.mhd',
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+ 'loc': '~/.cache/huggingface/datasets/downloads/extracted/59384cf05fc44e8c0cb23bb19e1fcd8f0c39720b282109d204a85561fe66bdb1/SIM/P2_5.0_fatty.8336179.1/1\\projection_DM1.loc',
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+ 'dcm': '~/.cache/huggingface/datasets/downloads/extracted/59384cf05fc44e8c0cb23bb19e1fcd8f0c39720b282109d204a85561fe66bdb1/SIM/P2_5.0_fatty.8336179.1/1\\DICOM_dm\\000.dcm',
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+ 'density': 'fatty',
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+ 'mass_radius': 5.0}
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+ ```
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+ Msynth dataset can also be loaded using custom breast density, mass redius, mass density, and relative dose information
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+
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+ ```python
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+ from datasets import load_dataset
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+
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+ # Dataset properties. change to 'all' to include all the values of breast density, mass redius, mass density, and relative dose information
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+ config_kwargs = {
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+ "lesion_density": ["1.0"],
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+ "dose": ["20%"],
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+ "density": ["fatty"],
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+ "size": ["5.0"]
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+ }
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+
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+ # Loading device data
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+ ds_data = load_dataset("didsr/msynth", 'device_data', **config_kwargs)
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+ # Loading segmentation-mask
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+ ds_seg = load_dataset("didsr/msynth", 'segmentation_mask', **config_kwargs)
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+ ```
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+ The meta data can also be loaded using the datasets API. An example of using metadata is given in **Demo:** [https://github.com/DIDSR/msynth-release/tree/master/examples](https://github.com/DIDSR/msynth-release/tree/master/examples)
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+
138
+ ```python
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+ from datasets import load_dataset
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+ # Loading metadata
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+ ds_meta = load_dataset("didsr/msynth", 'metadata')
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+
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+ # A sample data instance
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+ ds_meta['metadata'][0]
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+
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+ # Output
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+
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+ {'fatty': '~\\.cache\\huggingface\\datasets\\downloads\\extracted\\3ea85fc6b3fcc253ac8550b5d1b21db406ca9a59ea125ff8fc63d9b754c88348\\bounds\\bounds_fatty.npy',
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+ 'dense': '~\\.cache\\huggingface\\datasets\\downloads\\extracted\\3ea85fc6b3fcc253ac8550b5d1b21db406ca9a59ea125ff8fc63d9b754c88348\\bounds\\bounds_dense.npy',
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+ 'hetero': '~\\.cache\\huggingface\\datasets\\downloads\\extracted\\3ea85fc6b3fcc253ac8550b5d1b21db406ca9a59ea125ff8fc63d9b754c88348\\bounds\\bounds_hetero.npy',
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+ 'scattered': '~\\.cache\\huggingface\\datasets\\downloads\\extracted\\3ea85fc6b3fcc253ac8550b5d1b21db406ca9a59ea125ff8fc63d9b754c88348\\bounds\\bounds_scattered.npy'}
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+
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+ ```
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+
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+ ## Citation
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+ ```
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+ @article{sizikova2023knowledge,
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+ title={Knowledge-based in silico models and dataset for the comparative evaluation of mammography AI for a range of breast characteristics, lesion conspicuities and doses},
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+ author={Sizikova, Elena and Saharkhiz, Niloufar and Sharma, Diksha and Lago, Miguel and Sahiner, Berkman and Delfino, Jana G. and Badano, Aldo},
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+ journal={Advances in Neural Information Processing Systems},
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+ volume={},
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+ pages={},
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+ year={2023}
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+ }
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+ ```
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+
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+ ## Related Links
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+ 1. [Virtual Imaging Clinical Trial for Regulatory Evaluation (VICTRE)](https://www.fda.gov/medical-devices/science-and-research-medical-devices/victre-silico-breast-imaging-pipeline).
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+ 2. [FDA Catalog of Regulatory Science Tools to Help Assess New Medical Devices](https://www.fda.gov/medical-devices/science-and-research-medical-devices/catalog-regulatory-science-tools-help-assess-new-medical-devices).
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+ 3. A. Badano, C. G. Graff, A. Badal, D. Sharma, R. Zeng, F. W. Samuelson, S. Glick, K. J. Myers. [Evaluation of Digital Breast Tomosynthesis as Replacement of Full-Field Digital Mammography Using an In Silico Imaging Trial](http://dx.doi.org/10.1001/jamanetworkopen.2018.5474). JAMA Network Open 2018.
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+ 4. A. Badano, M. Lago, E. Sizikova, J. G. Delfino, S. Guan, M. A. Anastasio, B. Sahiner. [The stochastic digital human is now enrolling for in silico imaging trials—methods and tools for generating digital cohorts.](http://dx.doi.org/10.1088/2516-1091/ad04c0) Progress in Biomedical Engineering 2023.
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+ 5. E. Sizikova, N. Saharkhiz, D. Sharma, M. Lago, B. Sahiner, J. G. Delfino, A. Badano. [Knowledge-based in silico models and dataset for the comparative evaluation of mammography AI](https://github.com/DIDSR/msynth-release). NeurIPS 2023 Workshop on Synthetic Data Generation with Generative AI.
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