The authors propose an Asymmetric Transformer Decoder that uses tissue type and patch embeddings to predict six actionable lung cancer mutations from H&E slides, but the uploaded full text is a different document.
Benchmarking self-supervised learning on diverse pathology datasets
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Identifying actionable driver mutations in lung cancer using an efficient Asymmetric Transformer Decoder
The authors propose an Asymmetric Transformer Decoder that uses tissue type and patch embeddings to predict six actionable lung cancer mutations from H&E slides, but the uploaded full text is a different document.