Author

J. L. Riesterer

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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Bioengineering· 3 Cancer Biology· 3

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Research Focus:cell type annotation· 4 Approach:deep learning· 4 Assay:electronic health records· 4 Organism:human· 3 Assay:imaging· 3 Research Focus:benchmarking· 2 Approach:contrastive learning· 2 Research Focus:differential expression· 2

Frequent Co-Authors

G. Thibault· 6 shared J. W. Gray· 6 shared X. Song· 3 shared A. Chaurasia· 2 shared B. DeLaRosa· 2 shared K. GIANNIOS· 2 shared K. Stoltz· 2 shared L. Pagano· 2 shared

Activity by Year

2019-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

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6 shared papersBioengineering

G. Thibault

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6 papers

Recent·Most cited

Towards Generalizability and Robustness in Biological Object Detection in Electron Microscopy Images

K. GIANNIOS, A. Chaurasia, C. Bueno, J. L. Riesterer, L. Pagano, T. P. Lo, G. Thibault, J. W. Gray, X. Song, B. DeLaRosa

This study improves model generalizability in biological object detection using electron microscopy images through normalization and augmentation techniques, achieving a 29% average precision boost.

bioRxiv · Nov 27, 2023 · PreprintBioengineering

Efficient semi-supervised semantic segmentation of electron microscopy cancer images with sparse annotations

L. Pagano, G. Thibault, W. Bousselham, J. L. Riesterer, X. Song, J. W. Gray

The paper compares deep learning models for segmenting nuclei and nucleoli in cancer EM images using sparse labels and semi-supervised learning, showing improvements in 3D Dice scores.

bioRxiv · Oct 30, 2023Bioengineering

Robust Segmentation of Cellular Ultrastructure on Sparsely Labeled 3D Electron Microscopy Images using Deep Learning

A. Machireddy, G. Thibault, K. G. Loftis, K. Stoltz, C. E. Bueno, H. R. Smith, J. L. Riesterer, J. W. Gray, X. Song

This paper develops a deep learning method to segment cellular ultrastructures in 3D EM images, achieving high accuracy for nuclei and nucleoli in cancer biopsies.

bioRxiv · May 27, 2021Cancer Biology

Noise Reconstruction & Removal Network: A New Architecture to Denoise FIB-SEM Images

K. Giannios, A. Chaurasia, G. Thibault, J. L. Riesterer, E. Stempinski, T. Lo, B. DeLaRosa, J. W. Gray

The paper introduces an unsupervised Noise Reconstruction and Removal Network for denoising FIB-SEM images, achieving results comparable to or better than supervised methods.

bioRxiv · May 27, 2021 · PreprintBioengineering

An Omic and Multidimensional Spatial Atlas from Serial Biopsies of an Evolving Metastatic Breast Cancer

B. E. Johnson, A. L. Creason, J. M. Stommel, J. M. Keck, S. Parmar, C. B. Betts, A. Blucher, C. Boniface, E. Bucher, E. A. Burlingame, T. Camp, K. Chin, J. Eng, J. Estabrook, H. S. Feiler, , J. W. Gray

This paper presents an OMS Atlas from serial biopsies of a metastatic breast cancer patient, linking clinical data to multi-omic and spatial profiles to study therapy-associated heterogeneity and resistance mechanisms.

bioRxiv · Dec 3, 2020Cancer Biology

A workflow for visualizing human cancer biopsies using large-format electron microscopy

J. L. Riesterer, C. S. Lopez, E. S. Stempinski, M. Williams, K. Loftis, K. Stoltz, G. Thibault, C. Lanicault, T. Williams, J. W. Gray

This paper describes a workflow for 2D and 3D imaging of human cancer biopsies using electron microscopy to reveal ultrastructural details not visible with conventional 2D EM.

bioRxiv · Jun 19, 2019 · PreprintCancer Biology