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README.md

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-HistoGPT is a vision language foundation model for dermatopathology. The model takes multiple tissue sections from a patient as input and generates a highly accurate pathology report that includes the disease classification, tumor subtype prediction, tumor thickness estimation, and other important clinical information. Most importantly, HistoGPT is fully interpretable, as every word or phrase in the output text can be visualized in the original image.
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-We trained HistoGPT on a large-scale dataset of 6,000 patient-report pairs from over 12,000 whole slide images (WSIs) of over 150 different skin conditions (healthy, inflammatory, cancerous, ...) provided by the Department of Dermatology at the Technical University of Munich (TUM). To test our model, we extensively evaluated HistoGPT on five external cohorts from five different countries, including a dataset of 1,300 patient-report pairs from the Department of Dermatology at the University Hospital Münster (UKM).
+HistoGPT is a vision language model that generates highly accurate pathology reports from gigapixel whole slide images. The model takes multiple tissue sections from a patient as input and generates a highly accurate pathology report that includes disease classification, tumor subtype prediction, tumor thickness estimation, and other important clinical information. Most importantly, HistoGPT is fully interpretable, as every word or phrase in the output text can be visualized in the original image.
 
 The full code of our project is available on [GitHub](https://huggingface.co/marr-peng-lab/histogpt). At the moment only the model weights are hosted here on Hugging Face.