Year: 2024
Type: conference-paper
Abstract: Deep learning for automated cell imaging analysis has become a tool of choice to process large amounts of data. But many of these methods lack explainability, slowing down their deployment for tasks such as diagnosis. We present a prototype-based framework to analyze structural changes which addresses the specific challenges of explainability in the context of cell imaging. Our method relies on classification between two distinct cell populations in a weakly supervised context where no label for individual cells is available. Our model extracts typical features from each population, representing intra-cellular structure, and provides an explanation on its classification decision by creating visualization of the local textures corresponding to the structures of interest. We show a real application where it effectively highlights a change in the organization of the actin content of the cells.
Language: English
Subfield: Biophysics
Domain: Life Sciences
Open Access status: green
Funder: Agence Nationale de la Recherche
OpenAlex ID: W4402915517