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--- |
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license: apache-2.0 |
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tags: |
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- medical |
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- vision |
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--- |
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# Model Card for MedSAM |
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MedSAM is a fine-tuned version of [SAM](https://huggingface.co/docs/transformers/main/model_doc/sam) for the medical domain. |
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## Model Description |
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MedSAM was trained on a large-scale medical image segmentation dataset with 1,090,486 medical image-mask pairs collected from different publicly available sources including 15 imaging modalities and over 30 cancer types. |
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MedSAM was initialized with the pre-trained SAM model with the ViT-Base backbone. The prompt encoder's weights were frozen, while all trainable parameters in the image encoder and mask decoder were updated during training. |
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The training was performed for 100 epochs with a batch size of 160 using the AdamW optimizer with a learning rate of 10−4 and a weight decay of 0.01. |
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- **Repository:** [MedSAM Official GitHub Repository](https://github.com/bowang-lab/medsam) |
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- **Paper:** [Segment Anything in Medical Images](https://arxiv.org/abs/2304.12306v1) |
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## Usage |
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```python |
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import requests |
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import torch |
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import numpy as np |
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import matplotlib.pyplot as plt |
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from PIL import Image |
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from transformers import SamModel, SamProcessor |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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model = SamModel.from_pretrained("flaviagiammarino/medsam-vit-base").to(device) |
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processor = SamProcessor.from_pretrained("flaviagiammarino/medsam-vit-base") |
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img_url = "https://raw.githubusercontent.com/bowang-lab/MedSAM/main/assets/img_demo.png" |
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raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB") |
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input_boxes = [95., 255., 190., 350.] |
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inputs = processor(raw_image, input_boxes=[[input_boxes]], return_tensors="pt").to(device) |
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outputs = model(**inputs, multimask_output=False) |
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masks = processor.image_processor.post_process_masks(outputs.pred_masks.cpu(), inputs["original_sizes"].cpu(), inputs["reshaped_input_sizes"].cpu()) |
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def show_mask(mask, ax, random_color): |
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if random_color: |
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color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0) |
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else: |
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color = np.array([251/255, 252/255, 30/255, 0.6]) |
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h, w = mask.shape[-2:] |
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mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1) |
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ax.imshow(mask_image) |
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def show_box(box, ax): |
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x0, y0 = box[0], box[1] |
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w, h = box[2] - box[0], box[3] - box[1] |
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ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor="blue", facecolor=(0, 0, 0, 0), lw=2)) |
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fig, ax = plt.subplots(1, 2, figsize=(10, 5)) |
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ax[0].imshow(np.array(raw_image)) |
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show_box(input_boxes, ax[0]) |
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ax[0].set_title("Input Image and Bounding Box") |
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ax[0].axis("off") |
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ax[1].imshow(np.array(raw_image)) |
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show_mask(masks[0], ax=ax[1], random_color=False) |
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show_box(input_boxes, ax[1]) |
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ax[1].set_title("MedSAM Segmentation") |
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ax[1].axis("off") |
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plt.show() |
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``` |
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## Additional Information |
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### Licensing Information |
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The authors have released the model code and pre-trained checkpoints under the [Apache License 2.0](https://github.com/bowang-lab/MedSAM/blob/main/LICENSE). |
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### Citation Information |
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``` |
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@article{ma2023segment, |
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title={Segment anything in medical images}, |
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author={Ma, Jun and Wang, Bo}, |
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journal={arXiv preprint arXiv:2304.12306}, |
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year={2023} |
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} |
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``` |
