Create README.md

README.md

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+---
+license: cc0-1.0
+task_categories:
+- image-classification
+- image-segmentation
+tags:
+- medical
+pretty_name: M-SYNTH
+size_categories:
+- 10K<n<100K
+---
+# M-SYNTH
+
+<!-- Provide a quick summary of the dataset. -->
+
+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.
+
+## Dataset Details
+
+The dataset has the following characteristics:
+
+* Breast density: dense, heterogeneously dense, scattered, fatty
+* Mass radius (mm): 5.00, 7.00, 9.00
+* Mass density: 1.0, 1.06, 1.1 (ratio of radiodensity of the mass to that of fibroglandular tissue)
+* Relative dose: 20%, 40%, 60%, 80%, 100% of the clinically recommended dose for each density
+
+### Dataset Description
+
+<!-- Provide a longer summary of what this dataset is. -->
+
+- **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/)
+- **License:** Creative Commons 1.0 Universal License (CC0)
+
+### Dataset Sources
+
+<!-- Provide the basic links for the dataset. -->
+
+- **Code:** [https://github.com/DIDSR/msynth-release](https://github.com/DIDSR/msynth-release)
+- **Paper:** [https://neurips.cc/virtual/2023/poster/73701](https://neurips.cc/virtual/2023/poster/73701) 
+- **Demo:** [https://github.com/DIDSR/msynth-release/tree/master/examples](https://github.com/DIDSR/msynth-release/tree/master/examples)
+
+## Uses
+
+<!-- Address questions around how the dataset is intended to be used. -->
+
+M-SYNTH is intended to facilitate testing of AI with pre-computed synthetic mammography data. 
+
+### Direct Use
+
+<!-- This section describes suitable use cases for the dataset. -->
+
+M-SYNTH can be used to evaluate the effect of mass size and density, breast density, and dose on AI performance in lesion detection. 
+M-SYNTH can be used to either train or test  pre-trained AI models. 
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
+
+M-SYNTH cannot be used in lieu of real patient examples to make performance determinations.
+
+
+## Dataset Structure
+
+<!-- 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. -->
+
+M-SYNTH is organized into a directory structure that indicates the parameters. The folder
+```
+device_data_VICTREPhantoms_spic_[LESION_DENSITY]/[DOSE]/[BREAST_DENSITY]/2/[LESION_SIZE]/SIM/P2_[LESION_SIZE]_[BREAST_DENSITY].8337609.[PHANTOM_FILE_ID]/[PHANTOM_FILEID]/
+```
+contains image files imaged with the specified parameters. Note that only examples with odd PHANTOM_FILEID contain lesions, others do not.
+
+
+```
+$ tree data/device_data_VICTREPhantoms_spic_1.0/1.02e10/hetero/2/5.0/SIM/P2_5.0_hetero.8337609.1/1/
+
+data/device_data_VICTREPhantoms_spic_1.0/1.02e10/hetero/2/5.0/SIM/P2_5.0_hetero.8337609.1/1/
+├── DICOM_dm
+│   └── 000.dcm
+├── projection_DM1.loc
+├── projection_DM1.mhd
+└── projection_DM1.raw
+```
+Each folder contains mammogram data  that can be read from .raw format (.mhd contains supporting data), or DICOM (.dcm) format. 
+Coordinates of lesions can be found in .loc files. Segmentations are stored in .raw format and can be found in data/segmentation_masks/* .
+
+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. 
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+Simulation-based testing is constrained to the parameter variability represented in the object model and the acquisition system. 
+There is a risk of misjudging model performance if the simulated examples do not capture the variability in real patients. Please
+see the paper for a full discussion of biases, risks, and limitations.
+
+
+## Citation
+```
+@article{sizikova2023knowledge,
+  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},
+  author={Sizikova, Elena and Saharkhiz, Niloufar and Sharma, Diksha and Lago, Miguel and Sahiner, Berkman and Delfino, Jana G. and Badano, Aldo},
+  journal={Advances in Neural Information Processing Systems},
+  volume={},
+  pages={16764--16778},
+  year={2023}
+}
+```
+
+## Related Links
+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).
+2. [FDA Catalog of Regulatory Science Tools to Help Assess New Medical Device](https://www.fda.gov/medical-devices/science-and-research-medical-devices/catalog-regulatory-science-tools-help-assess-new-medical-devices).
+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.
+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.   
+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.