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Update README.md (#2)

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- Update README.md (cc461ef2c88e82f7dc96831a4c922a00fc1e3a24)


Co-authored-by: Ziyue Wang <ZiyueWang@users.noreply.huggingface.co>

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  1. README.md +33 -5
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  license: apache-2.0
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  pipeline_tag: image-segmentation
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  tags:
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- - medica
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  ---
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- Welcome Medical Adapters Zoo (Med-Adpt Zoo)!
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  ## Med-Adpt Zoo Map 🐘🐊🦍🦒🦨🦜🦥
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@@ -27,14 +27,42 @@ Check our paper: [Medical SAM Adapter](https://arxiv.org/abs/2304.12620) for the
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  ## Why
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- SAM (Segment Anything Model) is one of the most popular open model for the image segmentation. Unfortaintly, it does not perform well on the medical images.
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  An efficient way to solve it is using Adapters, i.e., some layers with a few parameters to be added to the pre-trained SAM model to fine-tune it to the target down-stream tasks.
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  Medical image segmentation includes many different organs, lesions, abnormalities as the targets.
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- So we are training different adapter for each of the target, and share them here for the easy usage in the community.
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  Download an adapter for your target disease—trained on organs, lesions, and abnormalities—and effortlessly enhance SAM.
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- One adapter tranfers your SAM into a medical domain expert. Give it a try!
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ## Authorship
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  license: apache-2.0
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  pipeline_tag: image-segmentation
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  tags:
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+ - medical
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  ---
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+ Welcome to Medical Adapters Zoo (Med-Adpt Zoo)!
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  ## Med-Adpt Zoo Map 🐘🐊🦍🦒🦨🦜🦥
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  ## Why
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+ SAM (Segment Anything Model) is one of the most popular open models for image segmentation. Unfortunately, it does not perform well on the medical images.
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  An efficient way to solve it is using Adapters, i.e., some layers with a few parameters to be added to the pre-trained SAM model to fine-tune it to the target down-stream tasks.
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  Medical image segmentation includes many different organs, lesions, abnormalities as the targets.
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+ So we are training different adapters for each of the targets, and sharing them here for the easy usage in the community.
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  Download an adapter for your target disease—trained on organs, lesions, and abnormalities—and effortlessly enhance SAM.
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+ One adapter transfers your SAM to a medical domain expert. Give it a try!
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+
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+ ## How to Use
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+
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+ 1. Download the code of our MedSAM-Adapter [here](https://github.com/KidsWithTokens/Medical-SAM-Adapter).
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+ 2. Download the weights of the original SAM model.
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+ 3. Load the original model and our adapter for downstream tasks.
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+
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+ ```python
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+ import torch
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+ import torchvision.transforms as transforms
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+
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+ import cfg
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+ from utils import *
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+
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+ # set your own configs
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+ args = cfg.parse_args()
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+ GPUdevice = torch.device('cuda', args.gpu_device)
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+
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+ # load the original SAM model
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+ net = get_network(args, args.net, use_gpu=args.gpu, gpu_device=GPUdevice, distribution = args.distributed)
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+
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+ # load task-specific adapter
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+ adapter_path = 'OpticCup_Fundus_SAM_1024.pth'
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+ adapter = torch.load(adapter_path)['state_dict']
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+ for name, param in adapter.items():
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+ if name in adapter:
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+ net.state_dict()[name].copy_(param)
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+ ```
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  ## Authorship
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