Author

L. Perreard

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

Biology Disciplines

Click to filter author papers

Pathology· 2

Signature Assays, Organisms & Methods

Click to filter · or open tag

Research Focus:clustering· 2 Approach:deep learning· 2 Organism:human· 2 Assay:spatial transcriptomics· 2 Research Focus:cancer & tumor microenvironment· 1 Research Focus:cell-cell communication· 1 Approach:contrastive learning· 1 Assay:imaging· 1

Frequent Co-Authors

B. Christensen· 2 shared F. Kolling· 2 shared J. Levy· 2 shared L. Vaickus· 2 shared M. Fatemi· 2 shared S. Palisoul· 2 shared Z. Azher· 2 shared A. Diallo· 1 shared

Activity by Year

2022-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 43 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

2 shared papersPathology

B. Christensen

Mutual co-authors in cluster

Author profile →

2 papers

Recent·Most cited

Spatial Omics Driven Crossmodal Pretraining Applied to Graph-based Deep Learning for Cancer Pathology Analysis

Z. Azher, M. Fatemi, Y. Lu, G. Srinivasan, A. Diallo, B. Christensen, L. Salas, F. Kolling, L. Perreard, S. Palisoul, L. Vaickus, J. Levy

This paper applies spatial transcriptomics data with contrastive crossmodal pretraining to improve graph-based deep learning models for cancer histopathology analysis.

bioRxiv · Jul 30, 2023 · PreprintPathology

Inferring Spatially Resolved Transcriptomics Data from Whole Slide Images for the Assessment of Colorectal Tumor Metastasis: A Feasibility Study

M. Fatemi, E. Feng, C. Sharma, Z. Azher, T. Goel, O. Ramwala, S. Palisoul, R. Barney, L. Perreard, F. Kolling, L. A. Salas, B. Christensen, G. Tsongalis, L. Vaickus, J. Levy

This study investigates using deep learning to predict spatial transcriptomics data from whole slide images to assess colorectal tumor metastasis, finding that convolutional networks outperform transformer and graph-based models.

bioRxiv · Nov 24, 2022 · PreprintPathology