Web framework and container-based execution architecture for reproducible deployment and benchmarking of image analysis workflows (Rubens et al., Cell Patterns, 2020).
User interfaces and data models for multiple modalities beyond histology incl. MALDI-IMS/multispectral imaging (Rubens et al., Proteomics Clin Appl, 2019).
Data analysis modules to study user behavior in educational settings (Vanhee et al., J. Pathology Informatics 2019).
Machine learning algorithms for morphometric change measurements e.g. in developmental and toxicological studies. See e.g. Kumar et al. (ECCV 2022 Bioimage Computing) and Vandaele et al. (Nature Scientific Reports, 2018).
Deep/Machine learning algorithms for diagnostic or for phenotyping, e.g. in developmental and toxicological studies. See e.g. Marée et al. (PRL 2016; ISBI 2016); Jeanray et al. (PLOS One 2015); Mormont et al. (CVPRW 2018, IEEE JBHI 2020).
Benchmarking of algorithms for cell counting in specific regions of interests within tissues.
Image analysis workflows e.g. to detect abnormal cells for early cytological diagnosis. See e.g. Delga et al., Acta Cytologica 2014; Mormont et al., 2016.
Self-training algorithms for exploiting sparse annotations (Mormont et al., ECCV AIMIA 2022) and algorithms for the quantification and delineation of tissue areas in whole tissue slides (Marée et al. ISBI 2014).