Author

A. Suvarna

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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Pathology· 2

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Organism:human· 2 Assay:imaging· 2 Assay:bulk RNA-seq· 1 Research Focus:cancer & tumor microenvironment· 1 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Approach:contrastive learning· 1

Frequent Co-Authors

B. Christensen· 2 shared J. Levy· 2 shared L. Vaickus· 2 shared B. Hunt· 1 shared G. Srinivasan· 1 shared J.-Q. Chen· 1 shared K. Samkoe· 1 shared L. Fu· 1 shared

Activity by Year

2022-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 35 mutual co-author links

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2 shared papersPathology

B. Christensen

Mutual co-authors in cluster

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2 papers

Recent·Most cited

DeltaAI: Semi-Autonomous Tissue Grossing Measurements and Recommendations using Neural Radiance Fields for Rapid, Complete Intraoperative Histological Assessment of Tumor Margins

A. Suvarna, R. Vempati, R. Chacko, G. Srinivasan, Y. Lu, B. Hunt, V. Torres, K. Samkoe, M. Davis, L. Fu, B. Christensen, L. Vaickus, M. LeBoeuf, J. Levy

DeltaAI uses Neural Radiance Fields to create 3D tissue models for faster and more accurate intraoperative tumor margin assessment in Mohs Micrographic Surgery.

bioRxiv · Aug 7, 2023 · PreprintPathology

Assessment of Emerging Pretraining Strategies in Interpretable Multimodal Deep Learning for Cancer Prognostication

Z. Azher, A. Suvarna, J.-Q. Chen, Z. Zhang, B. Christensen, L. Salas, L. Vaickus, J. Levy

This study evaluates pretraining strategies in an interpretable multimodal deep learning framework for cancer prognosis, showing that pretraining improves performance over standard methods.

bioRxiv · Nov 21, 2022Pathology