Author

J. Rosenthal

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

Signature Assays, Organisms & Methods

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Approach:deep learning· 3 Assay:imaging· 3 Research Focus:cell type annotation· 2 Research Focus:differential expression· 2 Research Focus:benchmarking· 1 Approach:contrastive learning· 1 Research Focus:drug response / drug discovery· 1 Approach:experimental assay· 1

Frequent Co-Authors

E. M. Van Allen· 3 shared J. Nyman· 3 shared R. Umeton· 3 shared B. Jiang· 2 shared N. Mehta· 2 shared S. N. Hari· 2 shared A. Chowdhury· 1 shared A. Miller· 1 shared

Activity by Year

2019-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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3 shared papersCancer Biology

E. M. Van Allen

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4 papers

Recent·Most cited

Spatially aware deep learning reveals tumor heterogeneity patterns that encode distinct kidney cancer states

J. Nyman, T. Denize, Z. Bakouny, C. Labaki, B. M. Titchen, K. Bi, S. N. Hari, J. Rosenthal, N. Mehta, B. Jiang, B. Sharma, K. Felt, R. Umeton, D. A. Braun, S. Rodig, , E. M. Van Allen

The paper uses spatially aware deep learning to identify tumor heterogeneity patterns in kidney cancer, revealing distinct states associated with different histological subtypes.

bioRxiv · Jan 18, 2023Cancer Biology

Building tools for machine learning and artificial intelligence in cancer research: best practices and a case study with the PathML toolkit for computational pathology

J. Rosenthal, R. Carelli, M. Omar, D. Brundage, H. Ella, J. Nyman, S. Hari, E. M. Van Allen, M. Luigi, R. Umeton, M. Loda

The paper introduces PathML, a computational pathology toolkit designed for scalability, standardization, and ease of use in cancer research, with applications demonstrated across diverse use-cases.

bioRxiv · Oct 21, 2021Cancer Biology

Examining Batch Effect in Histopathology as a Distributionally Robust Optimization Problem

S. N. Hari, J. Nyman, N. Mehta, F. Dietlein, J. Rosenthal, B. Jiang, A. Chowdhury, H. Elmarakeby, E. Sengupta, R. Umeton, E. M. Van Allen

This paper shows that using abstention in a patch-based classifier improves performance in histopathology tasks while reducing reliance on spurious features.

bioRxiv · Sep 14, 2021 · PreprintCancer Biology

AML ImmunoPET: 64Cu-DOTA-anti-CD33 PET-CT imaging of AML in Xenograft mouse model

S. S. Madabushi, J. L. Brooks, D. Zuro, B. Kumar, J. Sanchez, L. Echavarria Parra, M. Orellana, P. Vishwasrao, I. Nair, J. Chea, K. Poku, N. Bowles, A. Miller, T. Ebner, J. Molnar, , S. K. Hui

This paper develops a 64Cu-DOTA-anti-CD33 PET-CT imaging method for detecting AML in xenograft mouse models with high sensitivity and specificity, showing spatial heterogeneity in disease distribution.

bioRxiv · May 10, 2019 · PreprintCancer Biology