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View Code? Open in Web Editor NEWMultimodal-attention-based virtual mIF staining (MAS) system
Multimodal-attention-based virtual mIF staining (MAS) system
Platform: Python3.7 + Tensorflow 2.6.0 1. Run "main_MAE.py" file to pretrain feature extractors for AF and DAPI images, respectively. A random masked mechanism is employed, where a mask of random size is applied to each patch at a random location during MAE training in each epoch. Early stop is used to define the model's convergence. 2. Run "main_task1" file to train the MAS model for the staining task of CD3, CD20, FOXP3, and PD1 biomarkers. 3. Run "main_task2" file to transfer the MAS model for the adaption of CD8, CD163, and PDL1 biomarkers. 4. Use "test" file to implement a quick virtual staining for label-free whole slides with trained model. Please cite paper "Generative AI-Powered Virtual Multiplex Staining from Label-Free Fluorescence Images for Precision Medicine" if the code is helpful for your work. If you have any questions, please feel free to contact me through [email protected]
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