This model is used for high and low classification of survival period and disease-free survival period for pathological multimodal input.
How to Get Started with the Model
Use the code inference.py to get started with the model.
Evaluation
In inference.py, modify the model path and dataset path to do the model evaluation.
The model outputs the classification accuracy on two categories.
Data
The inputs of the model are the pathological image features and pathological text features extracted based on PLIP [A visual–language foundation model for pathology image analysis using medical Twitter]. You can organize your own dataset for evaluation according to this method.
Results
| pathological image | heat map of image | text and importance distribution |
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This links shows more visualized results corresponding to pathological images and text inputs. The visualized results include the heat map distribution of pathological images, the heat map distribution of pathological image patches, and the display of text importance distribution.