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Ali Foroughi Pour

As seen in: BioRxiv
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Articles

SAMPLER: Empirical distribution representations for rapid analysis of whole slide tissue images

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By Patience Mukashyaka, Todd Sheridan, Ali Foroughi Pour, Jeffrey H. Chuang
| BioRxiv
Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website.
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Deep Learning Features Encode Interpretable Morphologies within Histological Images

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By Brian White, Jonghanne Park, Todd Sheridan, Ali Foroughi Pour
| BioRxiv
New Results Ali Foroughi Pour, Brian S. White, Jonghanne Park, Todd B. Sheridan, View ORCID ProfileJeffrey H. Chuang doi: https://doi.org/10.1101/2021.08.16.456518 Abstract Convolutional neural networks (CNNs) are revolutionizing digital pathology by enabling machine learning-based classification of a variety of phenotypes from hematoxylin and eosin (H&E) whole slide images (WSIs), but the interpretation of CNNs remains difficult.
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