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Waibel, D.J.E. ; Röell, E. ; Rieck, B. ; Giryes, R.* ; Marr, C.

A Diffusion Model Predicts 3D Shapes from 2D Microscopy Images.

In: (Proceedings - International Symposium on Biomedical Imaging, 18-21 April 2023, Cartagena, Colombia). 345 E 47th St, New York, Ny 10017 Usa: Ieee, 2023. 5 (Proceedings - International Symposium on Biomedical Imaging ; 2023-April)
DOI
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
Diffusion models are a special type of generative model, capable of synthesising new data from a learnt distribution. We introduce DISPR, a diffusion-based model for solving the inverse problem of three-dimensional (3D) cell shape prediction from two-dimensional (2D) single cell microscopy images. Using the 2D microscopy image as a prior, DISPR is conditioned to predict realistic 3D shape reconstructions. To showcase the applicability of DISPR as a data augmentation tool in a feature-based single cell classification task, we extract morphological features from the red blood cells grouped into six highly imbalanced classes. Adding features from the DISPR predictions to the three minority classes improved the macro F1 score from F1macro = 55.2 ± 4.6% to F1macro = 72.2 ± 4.9%. We thus demonstrate that diffusion models can be successfully applied to inverse biomedical problems, and that they learn to reconstruct 3D shapes with realistic morphological features from 2D microscopy images.
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Publikationstyp Artikel: Konferenzbeitrag
Korrespondenzautor
ISSN (print) / ISBN 1945-7928
e-ISSN 1945-8452
Konferenztitel Proceedings - International Symposium on Biomedical Imaging
Konferzenzdatum 18-21 April 2023
Konferenzort Cartagena, Colombia
Quellenangaben Band: 2023-April, Heft: , Seiten: 5 Artikelnummer: , Supplement: ,
Verlag Ieee
Verlagsort 345 E 47th St, New York, Ny 10017 Usa
Nichtpatentliteratur Publikationen
Institut(e) Institute of AI for Health (AIH)
Förderungen European Research Council (ERC) under the European Union