C. L. Adams, Compound classification using image-based cellular phenotypes, Methods Enzymol, vol.414, pp.440-468, 2006.

H. Ajakan, Domain-adversarial neural networks, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01624607

S. J. Altschuler and L. F. Wu, Cellular heterogeneity: do differences make a difference?, Cell, vol.141, pp.559-563, 2010.

S. Ben-david, A theory of learning from different domains, Mach. Learn, vol.79, pp.151-175, 2010.

J. Boyd, Analysing double-strand breaks in cultured cells for drug screening applications by causal inference, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp.445-448, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01984323

W. Ieee and D. C. ,

J. C. Caicedo, Data-analysis strategies for image-based cell profiling, Nat. Methods, vol.14, pp.849-863, 2017.

R. Caruana, Multitask learning, Mach. Learn, vol.28, pp.41-75, 1997.

F. Chollet and Y. Ganin, Domain-adversarial training of neural networks, J. Mach. Learn. Res, vol.17, pp.2096-2030, 2015.

W. J. Godinez, A multi-scale convolutional neural network for phenotyping high-content cellular images, Bioinformatics, vol.33, pp.2010-2019, 2017.

S. A. Haney, High-content screening moves to the front of the line, Drug Discov. Today, vol.11, pp.889-894, 2006.

M. Held, Cellcognition: time-resolved phenotype annotation in high-throughput live cell imaging, Nat. Methods, vol.7, p.747, 2010.
URL : https://hal.archives-ouvertes.fr/hal-01431427

S. P. Horbach and W. Halffman, The ghosts of hela: how cell line misidentification contaminates the scientific literature, PLoS One, vol.12, p.186281, 2017.

S. Ioffe and C. Szegedy, Batch normalization: accelerating deep network training by reducing internal covariate shift, 2015.

T. R. Jones, Voronoi-based segmentation of cells on image manifolds, International Workshop on Computer Vision for Biomedical Image Applications, pp.535-543, 2005.

C. Kandaswamy, High-content analysis of breast cancer using single-cell deep transfer learning, J. Biomol. Screen, vol.21, pp.252-259, 2016.

O. Z. Kraus, Classifying and segmenting microscopy images with deep multiple instance learning, Bioinformatics, vol.32, pp.52-59, 2016.

Y. Liu, Multi-omic measurements of heterogeneity in hela cells across laboratories, Nat. Biotechnol, vol.37, p.314, 2019.

V. Ljosa, Annotated high-throughput microscopy image sets for validation, Nat. Methods, vol.9, pp.637-637, 2012.

V. Ljosa, Comparison of methods for image-based profiling of cellular morphological responses to small-molecule treatment, J. Biomol. Screen, vol.18, pp.1321-1329, 2013.

L. Loo, fastcluster: fast hierarchical, agglomerative clustering routines for R and python, Nat. Methods, 4, 445. Mü llner, vol.53, pp.1-18, 2007.

B. Neumann, Phenotypic profiling of the human genome by time-lapse microscopy reveals cell division genes, Nature, vol.464, p.721, 2010.
URL : https://hal.archives-ouvertes.fr/hal-01144034

N. Orlov, WND-CHARM: multi-purpose image classification using compound image transforms, Pattern Recogn. Lett, vol.29, pp.1684-1693, 2008.

F. Pedregosa, Scikit-learn: machine learning in Python, J. Mach. Learn. Res, vol.12, pp.2825-2830, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

R. Pepperkok and J. Ellenberg, High-throughput fluorescence microscopy for systems biology, Nat. Rev. Mol. Cell Biol, vol.7, pp.690-696, 2006.

Z. E. Perlman, Multidimensional drug profiling by automated microscopy, Science, vol.306, pp.1194-1198, 2004.

F. Rose, Compound functional prediction using multiple unrelated morphological profiling assays, SLAS Technol, vol.23, pp.243-251, 2018.
URL : https://hal.archives-ouvertes.fr/hal-02425375

B. Sadacca, New insight for pharmacogenomics studies from the transcriptional analysis of two large-scale cancer cell line panels, Sci. Rep, vol.7, pp.1-12, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01634180

S. Singh, Pipeline for illumination correction of images for high-throughput microscopy, J. Microsc, vol.256, pp.231-236, 2014.

M. D. Slack, Characterizing heterogeneous cellular responses to perturbations, Proc. Natl. Acad. Sci. USA, 105, pp.19306-19311, 2008.

C. Sommer, A deep learning and novelty detection framework for rapid phenotyping in high-content screening, Mol. Biol. Cell, vol.28, pp.3428-3436, 2017.

T. Tieleman and G. Hinton, Lecture 6.5-rmsprop: divide the gradient by a running average of its recent magnitude, COURSERA, vol.4, pp.26-31, 2012.

V. Uhlmann, CP-CHARM: segmentation-free image classification made accessible, J. Mach. Learn. Res, vol.17, pp.2579-2605, 2008.

S. Van-der-walt, scikit-image: image processing in Python, PeerJ, vol.2, p.453, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01071542

T. Walter, Automatic identification and clustering of chromosome phenotypes in a genome wide RNAi screen by time-lapse imaging, J. Struct. Biol, vol.170, pp.1-9, 2010.
URL : https://hal.archives-ouvertes.fr/hal-01427996

S. J. Warchal, Development of the theta comparative cell scoring method to quantify diverse phenotypic responses between distinct cell types, Assay Drug Dev. Technol, vol.14, pp.395-406, 2016.