Author

Madhavi Dipak Andhari

KU LeuvenORCID 0000-0002-7652-7859Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Approach:deep learning· 2 Assay:imaging· 2 Research Focus:cell type annotation· 1 Research Focus:dimensionality reduction· 1 Approach:experimental assay· 1 Research Focus:software / tool· 1 Assay:spatial transcriptomics· 1

Frequent Co-Authors

Alexandre ArnouldiD· 3 shared Asier AntoranziD· 3 shared Francesca Maria BosisioiD· 3 shared Frederik De SmetiD· 3 shared Pouya NazariiD· 3 shared Bart De MooriD· 2 shared Gautam Shankar· 2 shared Jon Pey· 2 shared

Activity by Year

2024-2025 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 41 mutual co-author links

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3 shared papersBioinformatics

Alexandre ArnouldORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

CONCLAVE: CONsensus CLustering with Annotation-Validation Extrapolation for cyclic multiplexed immunofluorescence data

Pouya Nazari, Alexandre Arnould, Madhavi Dipak Andhari, Maite Fontecha, Jorge Martín Hernández, Bart De Moor, Jon Pey, Frederik De Smet, Francesca Maria Bosisio, Asier Antoranz

CONCLAVE improves cell annotation in cyclic multiplexed immunofluorescence data by using consensus clustering and validation, enhancing accuracy and reproducibility.

bioRxiv · Nov 13, 2025 · PreprintBioinformatics

COLLAGE: COnsensus aLignment of muLtiplexing imAGEs

Asier Antoranz, Alexandre Arnould, Madhavi Dipak Andhari, Pouya Nazari, Gautam Shankar, Bart De Moor, Frederik De Smet, Francesca Maria Bosisio, Jon Pey

COLLAGE improves image alignment for multiplexed immunohistochemistry by breaking alignment into smaller puzzles, enabling parallel processing and reducing errors.

bioRxiv · Jul 15, 2024 · PreprintBioinformatics

QUAL-IF-AI: Quality Control of Immunofluorescence Images using Artificial Intelligence

Madhavi Dipak Andhari, Giulia Rinaldi, Pouya Nazari, Gautam Shankar, Nikolina Dubroja, Johanna Vets, Tessa Ostyn, Maxime Vanmechelen, Brecht Decraene, Alexandre Arnould, Willem Mestdagh, Bart De Moor, Frederik De Smet, Francesca Maria Bosisio, Asier Antoranz

QUAL-IF-AI is a deep learning tool that detects and manages artifacts in multiplex immunofluorescence images, achieving high accuracy and IoU across common artifact types.

bioRxiv · Jan 26, 2024 · PreprintBioinformatics