Commit ·
3b79864
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Parent(s):
Duplicate from didsr/msynth
Browse filesCo-authored-by: Elena Sizikova <evsizikova@users.noreply.huggingface.co>
This view is limited to 50 files because it contains too many changes. See raw diff
- .gitattributes +55 -0
- README.md +172 -0
- data/device_data_VICTREPhantoms_spic_1.0/1.02e10/hetero/2/5.0/SIM.zip +3 -0
- data/device_data_VICTREPhantoms_spic_1.0/1.02e10/hetero/2/7.0/SIM.zip +3 -0
- data/device_data_VICTREPhantoms_spic_1.0/1.02e10/hetero/2/9.0/SIM.zip +3 -0
- data/device_data_VICTREPhantoms_spic_1.0/1.22e10/scattered/2/5.0/SIM.zip +3 -0
- data/device_data_VICTREPhantoms_spic_1.0/1.22e10/scattered/2/7.0/SIM.zip +3 -0
- data/device_data_VICTREPhantoms_spic_1.0/1.22e10/scattered/2/9.0/SIM.zip +3 -0
- data/device_data_VICTREPhantoms_spic_1.0/1.33e10/fatty/2/5.0/SIM.zip +3 -0
- data/device_data_VICTREPhantoms_spic_1.0/1.33e10/fatty/2/7.0/SIM.zip +3 -0
- data/device_data_VICTREPhantoms_spic_1.0/1.33e10/fatty/2/9.0/SIM.zip +3 -0
- data/device_data_VICTREPhantoms_spic_1.0/1.63e10/scattered/2/5.0/SIM.zip +3 -0
- data/device_data_VICTREPhantoms_spic_1.0/1.63e10/scattered/2/7.0/SIM.zip +3 -0
- data/device_data_VICTREPhantoms_spic_1.0/1.63e10/scattered/2/9.0/SIM.zip +3 -0
- data/device_data_VICTREPhantoms_spic_1.0/1.73e09/dense/2/5.0/SIM.zip +3 -0
- data/device_data_VICTREPhantoms_spic_1.0/1.73e09/dense/2/7.0/SIM.zip +3 -0
- data/device_data_VICTREPhantoms_spic_1.0/1.78e10/fatty/2/5.0/SIM.zip +3 -0
- data/device_data_VICTREPhantoms_spic_1.0/1.78e10/fatty/2/7.0/SIM.zip +3 -0
- data/device_data_VICTREPhantoms_spic_1.0/1.78e10/fatty/2/9.0/SIM.zip +3 -0
- data/device_data_VICTREPhantoms_spic_1.0/2.04e09/hetero/2/5.0/SIM.zip +3 -0
- data/device_data_VICTREPhantoms_spic_1.0/2.04e09/hetero/2/7.0/SIM.zip +3 -0
- data/device_data_VICTREPhantoms_spic_1.0/2.04e10/scattered/2/5.0/SIM.zip +3 -0
- data/device_data_VICTREPhantoms_spic_1.0/2.04e10/scattered/2/7.0/SIM.zip +3 -0
- data/device_data_VICTREPhantoms_spic_1.0/2.04e10/scattered/2/9.0/SIM.zip +3 -0
- data/device_data_VICTREPhantoms_spic_1.0/2.22e10/fatty/2/5.0/SIM.zip +3 -0
- data/device_data_VICTREPhantoms_spic_1.0/2.22e10/fatty/2/7.0/SIM.zip +3 -0
- data/device_data_VICTREPhantoms_spic_1.0/2.22e10/fatty/2/9.0/SIM.zip +3 -0
- data/device_data_VICTREPhantoms_spic_1.0/3.47e09/dense/2/5.0/SIM.zip +3 -0
- data/device_data_VICTREPhantoms_spic_1.0/3.47e09/dense/2/7.0/SIM.zip +3 -0
- data/device_data_VICTREPhantoms_spic_1.0/4.08e09/hetero/2/5.0/SIM.zip +3 -0
- data/device_data_VICTREPhantoms_spic_1.0/4.08e09/hetero/2/7.0/SIM.zip +3 -0
- data/device_data_VICTREPhantoms_spic_1.0/4.08e09/scattered/2/5.0/SIM.zip +3 -0
- data/device_data_VICTREPhantoms_spic_1.0/4.08e09/scattered/2/7.0/SIM.zip +3 -0
- data/device_data_VICTREPhantoms_spic_1.0/4.08e09/scattered/2/9.0/SIM.zip +3 -0
- data/device_data_VICTREPhantoms_spic_1.0/4.44e09/fatty/2/5.0/SIM.zip +3 -0
- data/device_data_VICTREPhantoms_spic_1.0/4.44e09/fatty/2/7.0/SIM.zip +3 -0
- data/device_data_VICTREPhantoms_spic_1.0/4.44e09/fatty/2/9.0/SIM.zip +3 -0
- data/device_data_VICTREPhantoms_spic_1.0/5.20e09/dense/2/5.0/SIM.zip +3 -0
- data/device_data_VICTREPhantoms_spic_1.0/5.20e09/dense/2/7.0/SIM.zip +3 -0
- data/device_data_VICTREPhantoms_spic_1.0/6.12e09/hetero/2/5.0/SIM.zip +3 -0
- data/device_data_VICTREPhantoms_spic_1.0/6.12e09/hetero/2/7.0/SIM.zip +3 -0
- data/device_data_VICTREPhantoms_spic_1.0/6.94e09/dense/2/5.0/SIM.zip +3 -0
- data/device_data_VICTREPhantoms_spic_1.0/6.94e09/dense/2/7.0/SIM.zip +3 -0
- data/device_data_VICTREPhantoms_spic_1.0/8.16e09/hetero/2/5.0/SIM.zip +3 -0
- data/device_data_VICTREPhantoms_spic_1.0/8.16e09/hetero/2/7.0/SIM.zip +3 -0
- data/device_data_VICTREPhantoms_spic_1.0/8.16e09/scattered/2/5.0/SIM.zip +3 -0
- data/device_data_VICTREPhantoms_spic_1.0/8.16e09/scattered/2/7.0/SIM.zip +3 -0
- data/device_data_VICTREPhantoms_spic_1.0/8.16e09/scattered/2/9.0/SIM.zip +3 -0
- data/device_data_VICTREPhantoms_spic_1.0/8.67e09/dense/2/5.0/SIM.zip +3 -0
- data/device_data_VICTREPhantoms_spic_1.0/8.67e09/dense/2/7.0/SIM.zip +3 -0
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README.md
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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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<!-- Provide a quick summary of the dataset. -->
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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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## Dataset Details
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The dataset has the following characteristics:
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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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<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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### Dataset Description
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<!-- Provide a longer summary of what this dataset is. -->
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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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### Dataset Sources
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<!-- Provide the basic links for the dataset. -->
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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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| 44 |
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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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| 45 |
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## Uses
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| 47 |
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| 48 |
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<!-- Address questions around how the dataset is intended to be used. -->
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M-SYNTH is intended to facilitate testing of AI with pre-computed synthetic mammography data.
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| 51 |
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### Direct Use
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| 53 |
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<!-- This section describes suitable use cases for the dataset. -->
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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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| 57 |
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M-SYNTH can be used to either train or test pre-trained AI models.
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| 58 |
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| 59 |
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### Out-of-Scope Use
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| 60 |
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<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
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| 62 |
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| 63 |
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M-SYNTH cannot be used in lieu of real patient examples to make performance determinations.
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| 64 |
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| 65 |
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## Dataset Structure
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| 67 |
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| 68 |
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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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| 69 |
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| 70 |
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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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| 72 |
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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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| 73 |
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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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| 75 |
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| 76 |
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```
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| 78 |
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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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| 79 |
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| 80 |
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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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| 81 |
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├── DICOM_dm
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| 82 |
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│ └── 000.dcm
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| 83 |
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├── projection_DM1.loc
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| 84 |
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├── projection_DM1.mhd
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| 85 |
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└── projection_DM1.raw
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```
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| 87 |
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Each folder contains mammogram data that can be read from .raw format (.mhd contains supporting data), or DICOM (.dcm) format.
|
| 88 |
+
Coordinates of lesions can be found in .loc files. Segmentations are stored in .raw format and can be found in data/segmentation_masks/* .
|
| 89 |
+
|
| 90 |
+
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.
|
| 91 |
+
## Bias, Risks, and Limitations
|
| 92 |
+
|
| 93 |
+
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
|
| 94 |
+
|
| 95 |
+
Simulation-based testing is constrained to the parameter variability represented in the object model and the acquisition system.
|
| 96 |
+
There is a risk of misjudging model performance if the simulated examples do not capture the variability in real patients. Please
|
| 97 |
+
see the paper for a full discussion of biases, risks, and limitations.
|
| 98 |
+
|
| 99 |
+
## How to use it
|
| 100 |
+
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`.
|
| 101 |
+
The msynth dataset has three configurations: 1) device_data, 2) segmentation_mask, and 3) metadata
|
| 102 |
+
You can load and iterate through the dataset using the configurations with the following lines of code:
|
| 103 |
+
|
| 104 |
+
```python
|
| 105 |
+
from datasets import load_dataset
|
| 106 |
+
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
|
| 107 |
+
print(ds_data["device_data"])
|
| 108 |
+
|
| 109 |
+
# A sample data instance
|
| 110 |
+
|
| 111 |
+
{'Raw': '~\\.cache\\huggingface\\datasets\\downloads\\extracted\\59384cf05fc44e8c0cb23bb19e1fcd8f0c39720b282109d204a85561fe66bdb1\\SIM\\P2_5.0_fatty.8336179.1\\1\\projection_DM1.raw',
|
| 112 |
+
'mhd': '~/.cache/huggingface/datasets/downloads/extracted/59384cf05fc44e8c0cb23bb19e1fcd8f0c39720b282109d204a85561fe66bdb1/SIM/P2_5.0_fatty.8336179.1/1\\projection_DM1.mhd',
|
| 113 |
+
'loc': '~/.cache/huggingface/datasets/downloads/extracted/59384cf05fc44e8c0cb23bb19e1fcd8f0c39720b282109d204a85561fe66bdb1/SIM/P2_5.0_fatty.8336179.1/1\\projection_DM1.loc',
|
| 114 |
+
'dcm': '~/.cache/huggingface/datasets/downloads/extracted/59384cf05fc44e8c0cb23bb19e1fcd8f0c39720b282109d204a85561fe66bdb1/SIM/P2_5.0_fatty.8336179.1/1\\DICOM_dm\\000.dcm',
|
| 115 |
+
'density': 'fatty',
|
| 116 |
+
'mass_radius': 5.0}
|
| 117 |
+
```
|
| 118 |
+
Msynth dataset can also be loaded using custom breast density, mass redius, mass density, and relative dose information
|
| 119 |
+
|
| 120 |
+
```python
|
| 121 |
+
from datasets import load_dataset
|
| 122 |
+
|
| 123 |
+
# Dataset properties. change to 'all' to include all the values of breast density, mass redius, mass density, and relative dose information
|
| 124 |
+
config_kwargs = {
|
| 125 |
+
"lesion_density": ["1.0"],
|
| 126 |
+
"dose": ["20%"],
|
| 127 |
+
"density": ["fatty"],
|
| 128 |
+
"size": ["5.0"]
|
| 129 |
+
}
|
| 130 |
+
|
| 131 |
+
# Loading device data
|
| 132 |
+
ds_data = load_dataset("didsr/msynth", 'device_data', **config_kwargs)
|
| 133 |
+
# Loading segmentation-mask
|
| 134 |
+
ds_seg = load_dataset("didsr/msynth", 'segmentation_mask', **config_kwargs)
|
| 135 |
+
```
|
| 136 |
+
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)
|
| 137 |
+
|
| 138 |
+
```python
|
| 139 |
+
from datasets import load_dataset
|
| 140 |
+
# Loading metadata
|
| 141 |
+
ds_meta = load_dataset("didsr/msynth", 'metadata')
|
| 142 |
+
|
| 143 |
+
# A sample data instance
|
| 144 |
+
ds_meta['metadata'][0]
|
| 145 |
+
|
| 146 |
+
# Output
|
| 147 |
+
|
| 148 |
+
{'fatty': '~\\.cache\\huggingface\\datasets\\downloads\\extracted\\3ea85fc6b3fcc253ac8550b5d1b21db406ca9a59ea125ff8fc63d9b754c88348\\bounds\\bounds_fatty.npy',
|
| 149 |
+
'dense': '~\\.cache\\huggingface\\datasets\\downloads\\extracted\\3ea85fc6b3fcc253ac8550b5d1b21db406ca9a59ea125ff8fc63d9b754c88348\\bounds\\bounds_dense.npy',
|
| 150 |
+
'hetero': '~\\.cache\\huggingface\\datasets\\downloads\\extracted\\3ea85fc6b3fcc253ac8550b5d1b21db406ca9a59ea125ff8fc63d9b754c88348\\bounds\\bounds_hetero.npy',
|
| 151 |
+
'scattered': '~\\.cache\\huggingface\\datasets\\downloads\\extracted\\3ea85fc6b3fcc253ac8550b5d1b21db406ca9a59ea125ff8fc63d9b754c88348\\bounds\\bounds_scattered.npy'}
|
| 152 |
+
|
| 153 |
+
```
|
| 154 |
+
|
| 155 |
+
## Citation
|
| 156 |
+
```
|
| 157 |
+
@article{sizikova2023knowledge,
|
| 158 |
+
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},
|
| 159 |
+
author={Sizikova, Elena and Saharkhiz, Niloufar and Sharma, Diksha and Lago, Miguel and Sahiner, Berkman and Delfino, Jana G. and Badano, Aldo},
|
| 160 |
+
journal={Advances in Neural Information Processing Systems},
|
| 161 |
+
volume={},
|
| 162 |
+
pages={},
|
| 163 |
+
year={2023}
|
| 164 |
+
}
|
| 165 |
+
```
|
| 166 |
+
|
| 167 |
+
## Related Links
|
| 168 |
+
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).
|
| 169 |
+
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).
|
| 170 |
+
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.
|
| 171 |
+
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.
|
| 172 |
+
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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