Author

Frederick Matthew Howard

University of ChicagoORCID 0000-0002-6039-1141Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Approach:deep learning· 2 Research Focus:cell type annotation· 1 Research Focus:drug response / drug discovery· 1 Organism:human· 1 Assay:imaging· 1 Assay:spatial transcriptomics· 1 Approach:statistical model· 1

Frequent Co-Authors

Alexander T. PearsoniD· 2 shared James M. DolezaliD· 2 shared Sara E. KochannyiD· 2 shared Brad Feiger· 1 shared Charles M. PerouiD· 1 shared Cheng FaniD· 1 shared Dezheng HuoiD· 1 shared Galina F. KhramtsovaiD· 1 shared

Activity by Year

2021-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 35 mutual co-author links

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2 shared papersCancer Biology

Alexander T. PearsonORCID

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2 papers

Recent·Most cited

Cancer BiologyScience AdvancesPeer-reviewedNov 15, 2024

Generative Adversarial Networks Accurately Reconstruct Pan-Cancer Histology from Pathologic, Genomic, and Radiographic Latent Features

Frederick Matthew Howard, Hanna M. Hieromnimon, Siddhi Ramesh…·Alexander T. Pearson

This paper introduces HistoXGAN, a GAN that reconstructs cancer histology from latent features, showing it retains tumor grade, histologic subtype, and gene expression patterns across 29 cancer types.

35 citations

imagingdeep learning

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BioinformaticsNature CommunicationsPeer-reviewedJul 20, 2021

The Impact of Digital Histopathology Batch Effect on Deep Learning Model Accuracy and Bias

Frederick Matthew Howard, James M. Dolezal, Sara E. Kochanny…·Alexander T. Pearson

This paper shows that digital histopathology batch effects in TCGA data affect DL model accuracy and bias, and proposes a method to address this issue.

330 citationstop 1%

humanspatial transcriptomics

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