Description: Lung tissue images containing channels nuclei stain, auto fluorescent and two fluorescent stains of different drugs. Large tissue images of around 23’000px X 35´000px. 2318 labelled ROI images. 40 annotated examples for segmentation.
Provider: Johan Karlsson at Astra Zeneca
Responsible person: Håkan Wieslander
Publications: Wieslander et al. Deep learning and conformal prediction for hierarchical analysis of large-scale whole slide tissue images. Under Review
Description: Image frames captured while moving the microscope over a sample. For each field of view, corresponding high resolution frames are acquired. Various areas imaged where each area contains ~ 100 motion degraded frames of size 1024 x 1024 with corresponding 40-70 high resolution frames of size 2048 x 2048
Provider: Ida-Maria Sintorn at Vironova
Responsible person: Håkan Wieslander
Publications: Wieslander et al. TEM Image Restoration From Fast Image Stream. (Accepted for poster presentation at Swedish Symposium for Deep Learning 2020)
Provider: Alan Sabirsh at Astra Zeneca
Description: For nine wells and two fields of view images (each 2554 x 2154 pixels with a pixel resolution of 0.1625 𝜇m/pixel) were acquired every ten minutes for twelve hours. Images included four channels: brightfield; a cell counterstain (shown in purple above); LNP (yellow); and GFP (green). Three LNP doses were added (in triplicate) to the wells: 0, 31.6 and 316 ng mRNA/well (25ul). If the drug is successfully uptaken by the cell then GFP expression occurs.
Responsible person: Phil Harrison
Publications: Deep learning models for lipid-nanoparticle-based drug delivery.
