Assaf Zaritsky

The paper introduces Mask Interpreter, a method to improve interpretability of image-to-image translation models, validating biological relevance and identifying artifacts in in silico labeling.

The paper investigates the relationship between cell shape, protein expression, and tissue state using spatial imaging and machine learning, finding a bidirectional link and identifying p53-positive tumor cells.

This paper introduces a context-dependent model, CELTIC, to improve in silico organelle labeling in label-free microscopy images by accounting for cell context, enabling analysis of out-of-distribution data.

The paper uses FLIM to measure chromatin compaction in TNBC tissues and finds that spatial chromatin states predict resistance to neoadjuvant chemotherapy.

This paper introduces an anomaly-based representation for high-content image-based phenotypic cell profiling, improving reproducibility and classification while maintaining interpretability.

The paper introduces CISM, a method to identify spatial multicellular motifs associated with disease states in single-cell spatial data, showing they outperform existing methods in predicting disease states in TNBC and melanoma.

This study uses micropillar arrays to analyze F-actin polymerization dynamics in mouse embryonic fibroblasts, revealing synchronized adhesion site communication through spatiotemporal analysis.

This paper introduces DISCOVER, a generative model that disentangles latent spaces to provide interpretable explanations for image-based classification models in in vitro fertilization.

The paper identifies self-organized transient signaling hotspots in Drosophila hematopoiesis using Ca2+ signaling and computational analysis.

The paper uses sibling embryo data from the same IVF cohort to improve machine learning prediction of embryo implantation outcomes.

The paper shows that abscission is delayed in early zebrafish embryos until midblastula transition, allowing sibling cells to remain connected and coordinate developmental timing.

This paper presents standardization efforts for cell migration data to improve data sharing and analysis.