rad-dino

About

What rad-dino is

Model card for RAD-DINO RAD-DINO is a vision transformer model trained to encode chest X-rays using the self-supervised learning method DINOv2. Model description RAD-DINO is described in detail in Exploring Scalable Medical Image Encoders Beyond Text Supervision (F. Pérez-García, H. Sharma, S. Bond-Taylor, et al., 2025). - Developed by: Microsoft Health Futures - Model type: Vision transformer - License: MIT - Finetuned from model: dinov2-base Uses RAD-DINO is shared for research purposes only. It is not meant to be used for clinical practice. 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. Biases, risks, and 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. Installation Usage Encode an image Weights for fine-tuning We have released a checkpoint compatible with the original DINOv2 code to help researchers fine-tune our model. We can use the hub model and load the RAD-DINO weights. Let's clone the DINOv2 repository so we can import the code for the head. The weights of the head are also released: Configs and augmentation The configuration files ssldefaultconfig.yaml and vitb14cxr.yaml, and the augmentations module are also available in the repository to help researchers reproduce the training procedure with our hyperparameters. Training details Training data We used images from five public, deidentified chest X-ray datasets to train this checkpoint of RAD-DINO. Images in the validation and test sets used to train MAIRA were excluded from the training set of RAD-DINO. The list of image files used for training is available at ./trainingimages.csv

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

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Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys rad-dino for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (rad-dino below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/embeddings \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"rad-dino","input":"text to embed"}'

Details

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Papers

Licence

Commercial licence needed

Sources

Weights & code

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