Author

S. Kochanny

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Cancer Biology· 4 Pathology· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 4 Research Focus:cell type annotation· 3 Organism:human· 3 Assay:imaging· 3 Research Focus:benchmarking· 2 Research Focus:drug response / drug discovery· 2 Assay:bulk RNA-seq· 1 Assay:clinical cohort / trial· 1

Frequent Co-Authors

A. T. Pearson· 5 shared J. Dolezal· 5 shared F. M. Howard· 4 shared J. N. Kather· 3 shared A. Srisuwananukorn· 2 shared G. Khramtsova· 2 shared N. A. Cipriani· 2 shared S. Ramesh· 2 shared

Activity by Year

2019-2024 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 34 mutual co-author links

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5 shared papersCancer Biology

A. T. Pearson

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5 papers

Recent·Most cited

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

F. M. Howard, H. Hieromnimon, S. Ramesh, J. Dolezal, S. Kochanny, Q. Zhang, B. Feiger, J. Peterson, C. Fan, C. Perou, J. M. Vickery, M. Sullivan, K. Cole, G. Khramtsova, A. 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.

bioRxiv · Mar 22, 2024Cancer Biology

Validating a low-cost, open-source, locally manufactured workstation and computational pipeline for automated histopathology evaluation using deep learning

D. Choudhury, J. Dolezal, E. Dyer, S. Kochanny, S. Ramesh, F. M. Howard, J. R. Margalus, A. Schroeder, J. J. Schulte, M. C. Garassino, J. N. Kather, A. T. Pearson

The paper validates a low-cost, open-source workstation and computational pipeline for automated histopathology evaluation using deep learning, showing comparable performance to expensive institutional hardware.

bioRxiv · Apr 19, 2023 · PreprintPathology

Latent transcriptional programs reveal histology-encoded tumor features spanning tissue origins

H. M. Hieromnimon, J. Dolezal, K. Doytcheva, F. M. Howard, S. Kochanny, Z. Zhang, R. L. Grossman, K. Tanager, C. Wang, J. N. Kather, E. Izumchenko, N. A. Cipriani, E. J. Fertig, A. T. Pearson, S. J. Riesenfeld

This paper uses machine learning to identify latent transcriptional programs in squamous cell carcinomas from histology images, linking them to clinically relevant biological processes.

bioRxiv · Mar 22, 2023Cancer Biology

Multimodal Prediction of Breast Cancer Recurrence Assays and Risk of Recurrence

F. M. Howard, J. Dolezal, S. Kochanny, G. Khramtsova, J. Vickery, A. Srisuwananukorn, A. Woodard, N. Chen, R. Nanda, C. M. Perou, O. I. Olopade, D. Huo, A. T. Pearson

This paper develops a deep learning model that predicts breast cancer recurrence using digital histology and clinical factors, outperforming a clinical nomogram and identifying patients who may avoid genomic testing.

bioRxiv · Jul 7, 2022 · PreprintCancer Biology

Deep learning detects virus presence in cancer histology

J. N. Kather, J. Schulte, H. I. Grabsch, C. Loeffler, H. S. Muti, J. Dolezal, A. Srisuwananukorn, N. Agrawal, S. Kochanny, S. von Stillfried, P. Boor, T. Yoshikawa, D. Jaeger, C. Trautwein, P. Bankhead, , A. T. Pearson

This paper uses deep learning to detect the presence of HPV and EBV in cancer histology images, achieving high accuracy in predicting virus-associated tumors.

bioRxiv · Jul 5, 2019 · PreprintCancer Biology