File size: 9,705 Bytes
46d083f f8c75fd 36e4788 46d083f 5e7c362 46d083f 5e7c362 46d083f 5e7c362 46d083f e8051ea 46d083f 5e7c362 46d083f f8c75fd 46d083f 5e7c362 46d083f f8c75fd 5e7c362 46d083f 5e7c362 f8c75fd 5e7c362 46d083f 5e7c362 46d083f f8c75fd 46d083f e8051ea 46d083f 5e7c362 fed8c44 5e7c362 fed8c44 5e7c362 fed8c44 5e7c362 fed8c44 5e7c362 fed8c44 5e7c362 e8051ea fed8c44 5e7c362 e8051ea 5e7c362 e8051ea 5e7c362 e8051ea 5e7c362 fed8c44 5e7c362 46d083f 5e7c362 46d083f 5e7c362 46d083f 31b1baf 46d083f 5e7c362 46d083f 5e7c362 46d083f 5e7c362 46d083f 5e7c362 46d083f 5e7c362 46d083f 5e7c362 46d083f 5e7c362 46d083f 5e7c362 46d083f 5e7c362 46d083f 5e7c362 46d083f 5e7c362 46d083f 5e7c362 46d083f 5e7c362 46d083f 5e7c362 46d083f 31b1baf 46d083f 5e7c362 46d083f 5e7c362 46d083f 31b1baf 46d083f 5e7c362 46d083f 5e7c362 46d083f 5e7c362 23589fe 5e7c362 46d083f 5e7c362 46d083f 5e7c362 46d083f e8051ea |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 |
---
license: mit
library_name: transformers
---
# Model card for RAD-DINO
<!-- Provide a quick summary of what the model is/does. -->
## Model description
<!-- Provide a longer summary of what this model is. -->
RAD-DINO is a vision transformer model trained to encode chest X-rays using the self-supervised learning method [DINOv2](https://openreview.net/forum?id=a68SUt6zFt).
RAD-DINO is described in detail in [RAD-DINO: Exploring Scalable Medical Image Encoders Beyond Text Supervision (F. Pérez-García, H. Sharma, S. Bond-Taylor, et al., 2024)](https://arxiv.org/abs/2401.10815).
- **Developed by:** Microsoft Health Futures
- **Model type:** Vision transformer
- **License:** MIT
- **Finetuned from model:** [`dinov2-base`](https://huggingface.co/facebook/dinov2-base)
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
RAD-DINO is shared for research purposes only.
It is **not meant to be used for clinical practice**.
<!-- ### Downstream use -->
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
The model is a vision backbone that can be plugged to other models for downstream tasks.
Some potential uses are:
- Image classification, with a classifier trained on top of the `CLS` token
- Image segmentation, with a decoder trained using the patch tokens
- Clustering, using the image embeddings directly
- Image retrieval, using nearest neighbors of the CLS token
- Report generation, with a language model to decode text
Fine-tuning RAD-DINO is typically not necessary to obtain good performance in downstream tasks.
<!-- ### Out-of-scope use -->
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
## Biases, risks, and limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
RAD-DINO was trained with data from three countries, therefore it might be biased towards population in the training data.
Underlying biases of the training datasets may not be well characterized.
## Getting started
Let us first write an auxiliary function to download a chest X-ray.
```python
>>> import requests
>>> from PIL import Image
>>> def download_sample_image() -> Image.Image:
... """Download chest X-ray with CC license."""
... base_url = "https://upload.wikimedia.org/wikipedia/commons"
... image_url = f"{base_url}/2/20/Chest_X-ray_in_influenza_and_Haemophilus_influenzae.jpg"
... headers = {"User-Agent": "RAD-DINO"}
... response = requests.get(image_url, headers=headers, stream=True)
... return Image.open(response.raw)
...
```
Now let us download the model and encode an image.
```python
>>> import torch
>>> from transformers import AutoModel
>>> from transformers import AutoImageProcessor
>>>
>>> # Download the model
>>> repo = "microsoft/rad-dino"
>>> model = AutoModel.from_pretrained(repo)
>>>
>>> # The processor takes a PIL image, performs resizing, center-cropping, and
>>> # intensity normalization using stats from MIMIC-CXR, and returns a
>>> # dictionary with a PyTorch tensor ready for the encoder
>>> processor = AutoImageProcessor.from_pretrained(repo)
>>>
>>> # Download and preprocess a chest X-ray
>>> image = download_sample_image()
>>> image.size # (width, height)
(2765, 2505)
>>> inputs = processor(images=image, return_tensors="pt")
>>>
>>> # Encode the image!
>>> with torch.inference_mode():
>>> outputs = model(**inputs)
>>>
>>> # Look at the CLS embeddings
>>> cls_embeddings = outputs.pooler_output
>>> cls_embeddings.shape # (batch_size, num_channels)
torch.Size([1, 768])
```
If we are interested in the feature maps, we can reshape the patch embeddings into a grid.
We will use [`einops`](https://einops.rocks/) (install with `pip install einops`) for this.
```python
>>> def reshape_patch_embeddings(flat_tokens: torch.Tensor) -> torch.Tensor:
... """Reshape flat list of patch tokens into a nice grid."""
... from einops import rearrange
... image_size = processor.crop_size["height"]
... patch_size = model.config.patch_size
... embeddings_size = image_size // patch_size
... patches_grid = rearrange(flat_tokens, "b (h w) c -> b c h w", h=embeddings_size)
... return patches_grid
...
>>> flat_patch_embeddings = outputs.last_hidden_state[:, 1:] # first token is CLS
>>> reshaped_patch_embeddings = reshape_patch_embeddings(flat_patch_embeddings)
>>> reshaped_patch_embeddings.shape # (batch_size, num_channels, height, width)
torch.Size([1, 768, 16, 16])
```
## Training details
### Training data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
We used images from five public, deidentified chest X-ray datasets to train this checkpoint of RAD-DINO.
| Dataset | Num. images |
| --------- | ----------: |
| [MIMIC-CXR](https://www.nature.com/articles/s41597-019-0322-0) | 368 960 |
| [CheXpert](https://ojs.aaai.org/index.php/AAAI/article/view/3834) | 223 648 |
| [NIH-CXR](https://openaccess.thecvf.com/content_cvpr_2017/html/Wang_ChestX-ray8_Hospital-Scale_Chest_CVPR_2017_paper.html) | 112 120 |
| [PadChest](https://www.sciencedirect.com/science/article/abs/pii/S1361841520301614) | 136 787 |
| [BRAX](https://www.nature.com/articles/s41597-022-01608-8) | 41 260 |
Images in the validation and test sets used to train [MAIRA](https://arxiv.org/abs/2311.13668) were excluded from the training set of RAD-DINO.
The list of image files used for training is available at [`./training_images.csv`](./training_images.csv).
Note this checkpoint is different from the one in the paper, where some private data was used (and fewer GPUs).
The checkpoint shared here is trained for 35 000 iterations (the total number of iterations in the run was 100 000, but we selected this checkpoint using linear probing on the validation sets of the evaluation datasets described in the paper).
We used 16 nodes with 4 A100 GPUs each, and a batch size of 40 images per GPU.
### Training procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
We refer to the [manuscript](https://arxiv.org/abs/2401.10815) for a detailed description of the training procedure.
#### Preprocessing
All DICOM files were resized using B-spline interpolation so that their shorter size was 518, min-max scaled to [0, 255], and stored as PNG files.
#### Training hyperparameters
- **Training regime:** fp16 using PyTorch-FSDP mixed-precision.
<!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
Our evaluation is best described in the [manuscript](https://arxiv.org/abs/2401.10815).
<!-- ### Testing data, factors & metrics
#### Testing Data
[More Information Needed]
#### Factors
[More Information Needed]
#### Metrics
[More Information Needed]
### Results
[More Information Needed]
#### Summary -->
## Environmental impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
<!-- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). -->
<!-- Hardware type: A100 PCIe -->
<!-- Hours: 1d 16h = 40h -->
<!-- Cloud provider: Azure -->
<!-- Region: Italy North -->
- **Hardware type:** NVIDIA A100 GPUs
- **Hours used:** 40 hours/GPU × 16 nodes × 4 GPUs/node = 2560 GPU-hours
- **Cloud provider:** Azure
- **Compute region:** West US 2
- **Carbon emitted:** 222 kg CO₂ eq.
### Compute infrastructure
RAD-DINO was trained on [Azure Machine Learning](https://azure.microsoft.com/en-us/products/machine-learning).
#### Hardware
We used 16 `Standard_NC96ads_A100_v4` nodes with four NVIDIA A100 (80 GB) GPUs each.
#### Software
We leveraged the code in [DINOv2](https://openreview.net/forum?id=a68SUt6zFt) for training.
We used [SimpleITK](https://simpleitk.org/) and [Pydicom](https://pydicom.github.io/) for processing of DICOM files.
## Citation
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
```bibtex
@misc{perezgarcia2024raddino,
title={{RAD-DINO}: Exploring Scalable Medical Image Encoders Beyond Text Supervision},
author={Fernando Pérez-García and Harshita Sharma and Sam Bond-Taylor and Kenza Bouzid and Valentina Salvatelli and Maximilian Ilse and Shruthi Bannur and Daniel C. Castro and Anton Schwaighofer and Matthew P. Lungren and Maria Wetscherek and Noel Codella and Stephanie L. Hyland and Javier Alvarez-Valle and Ozan Oktay},
year={2024},
eprint={2401.10815},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
**APA:**
> Pérez-García, F., Sharma, H., Bond-Taylor, S., Bouzid, K., Salvatelli, V., Ilse, M., Bannur, S., Castro, D.C., Schwaighofer, A., Lungren, M.P., Wetscherek, M.T., Codella, N., Hyland, S.L., Alvarez-Valle, J., & Oktay, O. (2024). *RAD-DINO: Exploring Scalable Medical Image Encoders Beyond Text Supervision*. ArXiv, abs/2401.10815.
## Model card contact
Fernando Pérez-García ([`fperezgarcia@microsoft.com`](mailto:fperezgarcia@microsoft.com)).
|