3 citations · 4 across the 3 of their papers we have counts for
3 papers
cs.LG2024★ 3 cited
Discovering interpretable models of scientific image data with deep learning
Christopher J. Soelistyo, Alan R. Lowe
How can we find interpretable, domain-appropriate models of natural phenomena given some complex, raw data such as images? Can we use such models to derive scientific insight from…
cs.CV2022★ 1 cited
Affinity-VAE: incorporating prior knowledge in representation learning from scientific images
Marjan Famili, Jola Mirecka, Camila Rangel Smith +8
Learning compact and interpretable representations of data is a critical challenge in scientific image analysis. Here, we introduce Affinity-VAE, a generative model that enables us…
q-bio.TO2019
Single-cell approaches to cell competition: high-throughput imaging, machine learning and simulations
Daniel Gradeci, Anna Bove, Guillaume Charras +2
Cell competition is a quality control mechanism in tissues that results in the elimination of less fit cells. Over the past decade, the phenomenon of cell competition has been iden…