metadata

license: mit
language:
  - en
pipeline_tag: image-text-to-text
tags:
  - medical
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  This model and associated code are released under the mit license and may only
  be used for non-commercial, academic research purposes with proper
  attribution. Any commercial use, sale, or other monetization of the SongCi
  model and its derivatives, which include models trained on outputs from the
  SongCi model or datasets created from the SongCi model, is prohibited and
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  copy of the model. If another user within your organization wishes to use the
  SongCi model, they must register as an individual user and agree to comply
  with the terms of use. Users may not attempt to re-identify the deidentified
  data used to develop the underlying model. If you are a commercial entity,
  please contact the corresponding author.
extra_gated_fields:
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    type: select
    options:
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        value: other
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base_model:
  - vinid/plip

SongCi

[Github Repo]

SongCi is a multi-modal deep learning model tailored for forensic pathological analyses. The architecture consists of three main parts, i.e., an imaging encoder for WSI feature extraction, a text encoder for the embedding of gross key findings as well as diagnostic queries, and a multi-modal fusion block that integrates the embeddings of WSI and gross key findings to align with those of the diagnostic queries.

How to use SongCi ?

patch-level feature extraction


import vision_former as vits
import torch

model = vits.__dict__['vit_small'](patch_size=16, num_classes=0)
model.load_state_dict(torch.load("./songci.pth"))


for p in model.parameters():

    p.requires_grad = False


model.eval()

aa=torch.randn((10,3,224,224))

print(model(aa).shape)

multi-modality fusion


from model_fusion_plip import fusionblock2,fusionblock_wonum
import torch

from transformers import  CLIPModel

def model_fusion(depth=2,noise_ratio=0.5, gate=True,num_em=True):
    prototype_all = torch.load("songci_prototype.pt",map_location="cuda") # import the prototype space
    disease_model = CLIPModel.from_pretrained("vinid/plip")
    disease_model.eval()

    if num_em == True:

        model_fusion = fusionblock2(prototype_all=prototype_all, text_model=disease_model, disease_model=disease_model, depth=depth, noise_ratio=noise_ratio, gated=gate)
    else:
        model_fusion = fusionblock_wonum(prototype_all=prototype_all, text_model=disease_model, disease_model=disease_model,
                                    depth=depth, noise_ratio=noise_ratio, gated=gate)

    return model_fusion

model = model_fusion()

model.load_state_dict(torch.load("fusion_checkpoint.pth",map_location="cpu"))
print("finish!!!!")

License and Terms of Use

This model and associated code are released under the MIT license and may only be used for non-commercial, academic research purposes with proper attribution. Any commercial use, sale, or other monetization of the SongCi model and its derivatives, which include models trained on outputs from the SongCi model or datasets created from the SongCi model, is prohibited and requires prior approval. Downloading the model requires prior registration on Hugging Face and agreeing to the terms of use. By downloading this model, you agree not to distribute, publish or reproduce a copy of the model. If another user within your organization wishes to use the SongCi model, they must register as an individual user and agree to comply with the terms of use. Users may not attempt to re-identify the deidentified data used to develop the underlying model. If you are a commercial entity, please contact the corresponding author.

Contact

For any additional questions or comments, contact Chunfeng Lian ([email protected]), Chen Shen ([email protected]).

BibTeX

@misc{shen2024largevocabularyforensicpathologicalanalyses,
      title={Large-vocabulary forensic pathological analyses via prototypical cross-modal contrastive learning}, 
      author={Chen Shen and Chunfeng Lian and Wanqing Zhang and Fan Wang and Jianhua Zhang and Shuanliang Fan and Xin Wei and Gongji Wang and Kehan Li and Hongshu Mu and Hao Wu and Xinggong Liang and Jianhua Ma and Zhenyuan Wang},
      year={2024},
      eprint={2407.14904},
      archivePrefix={arXiv},
      primaryClass={eess.IV},
      url={https://arxiv.org/abs/2407.14904}, 
}