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Asier Rabasco

As seen in: Cancer Research
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Articles

Counterfactual Diffusion Models Provide Interpretable Explanations of Artificial Intelligence Models in Pathology Open Access

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By Asier Rabasco, Jakob Nikolas
| Cancer Research Verified
Open figure viewer Deep learning (DL) can predict diagnostic and prognostic biomarkers from hematoxylin and eosin (H&E)–stained whole-slide images (WSI) derived from cancer pathology slides (1–4). DL models perform well for many clinically relevant tasks, such as making diagnoses or extracting subtle visual information related to biomarkers (5).
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Counterfactual Diffusion Models Provide Interpretable Explanations of Artificial Intelligence Models in Pathology Open Access

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By Nic Gabriel, Katherine Jane, Asier Rabasco
| Cancer Research Verified
Open figure viewer This content is only available via PDF. This open access article is distributed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license This work is licensed under a Creative Commons Attribution 4.0 International License .
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