Sampaio, PauloPauloSampaioScandella, DavideDavideScandellaPatty, C. H. LucasC. H. LucasPatty0000-0002-0073-8879Di Fazio, HeatherHeatherDi FazioMarquez-Neila, PabloPabloMarquez-NeilaCenteno Ramos, IreneIreneCenteno RamosWartenberg, MartinMartinWartenberg0000-0002-5378-3825Storni, FedericoFedericoStorniDemory, Brice-OlivierBrice-OlivierDemory0000-0002-9355-5165Candinas, DanielDanielCandinasPerren, AurelAurelPerren0000-0002-6819-6092Sznitman, RaphaelRaphaelSznitman0000-0001-6791-47532025-11-102025-11-102025-09-01https://boris-portal.unibe.ch/handle/20.500.12422/223038Mueller Matrix polarimetry (MMP) characterizes changes in light polarization after interacting with a medium, providing insights into tissue microstructure. Combined with multispectral (MS) imaging cameras, MS-MMP offers a novel way to quickly and safely acquire tissue surface information. Machine learning methodologies enable new diagnostic methods by automating tasks on fresh tissue biopsies, though this requires extensive and diverse data. To achieve this, we propose a user-friendly MS-MMP imager with a simple interface and fast acquisition time operated by laboratory technicians and residents. We show that our system, when operated by laboratory staff over several months, yields highquality data in large amounts and with positive feedback of its inclusion in a clinically compliant workflow. This positive outcome is promising for such systems to be used for large data collection initiatives.enarticle10.48620/9228410.1515/cdbme-2025-0144