Author

L. Salas

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· 4 Ecology· 1

Signature Assays, Organisms & Methods

Click to filter · or open tag

Assay:imaging· 5 Organism:human· 4 Approach:deep learning· 3 Research Focus:cancer & tumor microenvironment· 2 Research Focus:cell type annotation· 2 Research Focus:cell-cell communication· 2 Approach:contrastive learning· 2 Assay:spatial transcriptomics· 2

Frequent Co-Authors

B. Christensen· 4 shared J. Levy· 4 shared L. Vaickus· 4 shared F. Kolling· 3 shared S. Palisoul· 3 shared Z. Azher· 3 shared A. Diallo· 2 shared G. Srinivasan· 2 shared

Activity by Year

2020-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 45 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

4 shared papersPathology

B. Christensen

Mutual co-authors in cluster

Author profile →

5 papers

Recent·Most cited

Potential to Enhance Large Scale Molecular Assessments of Skin Photoaging through Virtual Inference of Spatial Transcriptomics from Routine Staining

G. Srinivasan, M. Davis, M. LeBoeuf, M. Fatemi, Z. Azher, Y. Lu, A. Diallo, M. Montivero, F. Kolling, L. Perrard, L. Salas, B. Christensen, S. Palisoul, G. Tsongalis, L. Vaickus, , J. Levy

This paper develops machine learning models to infer spatial transcriptomics data from H&E-stained skin slides, improving the assessment of photoaging and its therapeutic implications.

bioRxiv · Jul 30, 2023 · PreprintPathology

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

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

Graph neural networks ameliorate potential impacts of imprecise large-scale autonomous immunofluorescence labeling of immune cells on whole slide images

R. Reddy, R. Reddy, C. Sharma, C. Jackson, S. Palisoul, R. Barney, F. Kolling, L. Salas, B. Christensen, G. Tsongalis, L. Vaickus, J. Levy

This study uses graph neural networks to improve TIL classification on whole slide images by leveraging contextual features from immunofluorescence labeling.

bioRxiv · Aug 28, 2022 · PreprintPathology

Reducing error and increasing reliability of wildlife counts from citizen science surveys: counting Weddell Seals in the Ross Sea from satellite images

L. Salas, M. LaRue, N. Nur, D. Ainley, S. Stammerjohn, J. Pennycook, J. Rotella, J. T. Paterson, D. Siniff, K. Stamatiou, M. Dozier, J. Saints, L. Barrington

The paper improves wildlife count reliability from citizen science surveys by developing a correction method for satellite images of Weddell Seals in the Ross Sea.

bioRxiv · Nov 18, 2020 · PreprintEcology