Publications

1. T. Lazard, M. Lerousseau, S. Gardrat, A. Vincent-Salomon, M.-H. Stern, M. Rodrigues, E. Decencière, and T. Walter, “Democratizing computational pathology: Optimized Whole Slide Image representations for The Cancer Genome Atlas,” BioRXiv, Preprint, Dec. 2023. doi: 10.1101/2023.12.04.569894.
2. T. Defard, H. Laporte, M. Ayan, S. Juliette, S. Curras-Alonso, C. Weber, F. Massip, J.-A. Londoño-Vallejo, C. Fouillade, F. Mueller, and T. Walter, “A point cloud segmentation framework for image-based spatial transcriptomics,” BioRXiv, Preprint, Dec. 2023. doi: 10.1101/2023.12.01.569528.
3. Lazard, Tristan, Bataillon, Guillaume, Walter, Thomas, and Vincent Salomon, Anne, “Cancer du sein - utilisation de l’intelligence artificielle pour prédire le statut tumoral relatif à la recombinaison homologue,” Med Sci (Paris), vol. 39, no. 12, pp. 926–928, 2023, doi: 10.1051/medsci/2023169.
4. M. Lubrano et al., “Diagnosis with confidence: Deep learning for reliable classification of laryngeal dysplasia,” Histopathology, p. his.15067, Oct. 2023, doi: 10.1111/his.15067.
5. D. B. Page et al., “Spatial analyses of immune cell infiltration in cancer: Current methods and future directions: A report of the International Immuno‐Oncology Biomarker Working Group on Breast Cancer,” The Journal of Pathology, vol. 260, no. 5, pp. 514–532, Aug. 2023, doi: 10.1002/path.6165.
6. J. Thagaard et al., “Pitfalls in machine learning‐based assessment of tumor‐infiltrating lymphocytes in breast cancer: A report of the International Immuno‐Oncology Biomarker Working Group on Breast Cancer,” The Journal of Pathology, vol. 260, no. 5, pp. 498–513, Aug. 2023, doi: 10.1002/path.6155.
7. M. Lubrano, Y. Bellahsen-Harrar, R. Fick, C. Badoual, and T. Walter, “Simple and Efficient Confidence Score for Grading Whole Slide Images” in Proceedings of Machine Learning Research, Jul. 2023, vol. 58, pp. 1–19.link
8. D. Zyss, S. A. Ribeiro, M. J. C. Ludlam, T. Walter, and A. Fehri, “Cell Segmentation in Images Without Structural Fluorescent Labels,” Biol. Imaging, pp. 1–18, Jul. 2023, doi: 10.1017/S2633903X23000168.
9. Y. Bellahsen-Harrar et al., “AI-Augmented Pathology for Head and Neck Squamous Lesions Improves Non-HN Pathologist Agreement to Expert Level,” Pathology, preprint, Jul. 2023. doi: 10.1101/2023.07.23.23292962.
10. T. Lazard, M. Lerousseau, E. Decencière, and T. Walter, “Giga-SSL: Self-supervised learning for gigapixel images,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR) workshops, Jun. 2023, pp. 4304–4313. link
11. S. Curras-Alonso et al., “An interactive murine single-cell atlas of the lung responses to radiation injury,” Nat Commun, vol. 14, no. 1, p. 2445, Apr. 2023, doi: 10.1038/s41467-023-38134-z.
12. M. Lubrano, Y. Bellahsen-Harrar, R. Fick, C. Badoual, and T. Walter, “Simple and efficient confidence score for grading whole slide images.” arXiv, Mar. 08, 2023. doi: 10.48550/arXiv.2303.04604
13. T. Bonte, M. Philbert, E. Coleno, E. Bertrand, A. Imbert, and T. Walter, “Learning with Minimal Effort: Leveraging in Silico Labeling for Cell and Nucleus Segmentation,” in Computer Vision – ECCV 2022 Workshops, 2023, pp. 423–436, doi: 10.1007/978-3-031-25069-9_28.
14. A. Imbert, F. Mueller, and T. Walter, “PointFISH: Learning Point Cloud Representations for RNA Localization Patterns,” in Computer Vision – ECCV 2022 Workshops, 2023, pp. 487–502, doi: 10.1007/978-3-031-25069-9_32.
15. M. Lubrano et al., “Automatic Grading of Cervical Biopsies by Combining Full and Self-supervision,” in Computer Vision – ECCV 2022 Workshops, 2023, pp. 408–423, doi: 10.1007/978-3-031-25082-8_27.
16. A. Imbert, F. Mueller, and T. Walter, “PointFISH – learning point cloud representations for RNA localization patterns.” arXiv, Feb. 21, 2023, Accessed: Mar. 11, 2023. doi: 10.48550/arXiv.2302.10923
17. T. Bonte, M. Philbert, E. Coleno, E. Bertrand, A. Imbert, and T. Walter, “Learning with minimal effort: Leveraging in silico labeling for cell and nucleus segmentation.” arXiv, Jan. 10, 2023. doi: 10.48550/arXiv.2301.03914
18. T. Lazard, M. Lerousseau, E. Decencière, and T. Walter, “Giga-SSL: Self-Supervised Learning for Gigapixel Images.” arXiv, Dec. 2022, doi: 10.48550/arXiv.2212.03273.
19. M. Lubrano, Y. Bellahsen-Harrar, S. Berlemont, T. Walter, and C. Badoual, “Diagnosis with Confidence: Deep Learning for Reliable Classification of Squamous Lesions of the Upper Aerodigestive Tract,” BioRxiv, preprint, Dec. 2022. doi: 10.1101/2022.12.21.521392.
20. T. Lazard et al., “Deep learning identifies morphological patterns of homologous recombination deficiency in luminal breast cancers from whole slide images,” Cell Reports Medicine, p. 100872, Dec. 2022, doi: 10.1016/j.xcrm.2022.100872.
21. A. Safieddine et al., “HT-smFISH: A cost-effective and flexible workflow for high-throughput single-molecule RNA imaging,” Nature Protocols, Oct. 2022, doi: 10.1038/s41596-022-00750-2.
22. A. Imbert et al., “FISH-quant v2: A scalable and modular tool for smFISH image analysis,” RNA, p. rna.079073.121, Mar. 2022, doi: 10.1261/rna.079073.121.
23. P. Naylor et al., “Prediction of Treatment Response in Triple Negative Breast Cancer From Whole Slide Images,” Frontiers in Signal Processing, vol. 2, p. 851809, Jun. 2022, doi: 10.3389/frsip.2022.851809.
24. M. Lubrano di Scandalea et al., “Automatic grading of cervical biopsies by combining full and self-supervision,” bioRxiv, p. 2022.01.14.476330, Jan. 2022, doi: 10.1101/2022.01.14.476330.
25. T. Lazard et al., “Deep learning identifies new morphological patterns of homologous recombination deficiency in luminal breast cancers from whole slide images,” bioRxiv, 2021, doi: 10.1101/2021.09.10.459734
26. A. Imbert et al., “FISH-quant v2: A scalable and modular analysis tool for smFISH image analysis,” bioRxiv, 2021, doi: 10.1101/2021.07.20.453024.
27. A. Safieddine et al., “A choreography of centrosomal mRNAs reveals a conserved localization mechanism involving active polysome transport,” Nature Communications, vol. 12, no. 1, p. 1352, Mar. 2021, doi: 10.1038/s41467-021-21585-7.
28. S. Curras-Alonso, J. Soulier, T. Walter, F. Mueller, A. Londoño-Vallejo, and C. Fouillade, “Spatial transcriptomics for respiratory research and medicine,” Eur Respir J, p. 2004314, Jan. 2021, doi: 10.1183/13993003.04314-2020.
29. X. Pichon et al., “The kinesin KIF1C transports APC-dependent mRNAs to cell protrusions,” RNA, 2020, doi:10.1261/rna.078576.120.
30. M. Balluet et al., “Neural network fast-classifies biological images using features selected after their random-forests-importance to power smart microscopy.” 2020, [Online]. Available: http://arxiv.org/abs/2012.10331.
31. R. Chouaib et al., “A Dual Protein-mRNA Localization Screen Reveals Compartmentalized Translation and Widespread Co-translational RNA Targeting,” Developmental Cell, vol. 54, no. 6, pp. 773–791.e5, Sep. 2020, doi: 10.1016/j.devcel.2020.07.010.
32. T. Walter, “Bioimage Informatics for Phenomics,” Habilitation à diriger des recherches, Sorbonne Université, 2020,tel-02981391.
33. J. Boyd, Z. Gouveia, F. Perez, and T. Walter, “Experimentally-Generated Ground Truth for Detecting Cell Types in an Image-Based Immunotherapy Screen,” in 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), Apr. 2020, pp. 886–890, doi: 10.1109/ISBI45749.2020.9098696.
34. J. C. Boyd, A. Pinheiro, E. D. Nery, F. Reyal, and T. Walter, “Domain-invariant features for mechanism of action prediction in a multi-cell-line drug screen,” Bioinformatics, Oct. 2019, doi: 10.1093/bioinformatics/btz774.
35. M. Durand et al., “Human lymphoid organ cDC2 and macrophages play complementary roles in T follicular helper responses,” The Journal of Experimental Medicine, vol. 216, no. 7, pp. 1561 LP–1581, Jul. 2019, doi: 10.1084/jem.20181994.
36. R. Dubois et al., “A deep learning approach to identify mRNA localization patterns,” in IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), 2019, pp. 1386–1390, doi: 10.1109/ISBI.2019.8759235.
37. I. Kuperstein and E. Barillot, Eds., Computational Systems Biology Approaches in Cancer Research. Chapman and Hall/CRC, 2019.
38. P. Naylor, J. Boyd, M. Laé, F. Reyal, and T. Walter, “Predicting Residual Cancer Burden In A Triple Negative Breast Cancer Cohort,” in 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), 2019, pp. 933–937, doi: 10.1109/ISBI.2019.8759205.
39. P. Naylor, M. La, F. Reyal, and T. Walter, “Segmentation of Nuclei in Histopathology Images by deep regression of the distance map,” IEEE Transactions on Medical Imaging, vol. 38, no. 2, pp. 448–459, 2019, doi: 10.1109/TMI.2018.2865709.
40. J. Boyd, A. Pinhiero, E. D. Nery, F. Reyal, and T. Walter, “Analysing double-strand breaks in cultured cells for drug screening applications by causal inference,” in International Symposium on Biomedical Imaging (ISBI): From Nano to Macro, 2018, vol. 2018–April, pp. 445–448, doi: 10.1109/ISBI.2018.8363612.
41. A. Samacoits et al., “A computational framework to study sub-cellular RNA localization,” Nature Communications, vol. 9, no. 1, p. 4584, 2018, doi: 10.1038/s41467-018-06868-w.
42. E. Bernard, Y. Jiao, E. Scornet, V. Stoven, T. Walter, and J.-P. Vert, “Kernel multitask regression for toxicogenetics,” Molecular Informatics, vol. 36, Jan. 2017, [Online]. Available: http://biorxiv.org/content/early/2017/08/01/171298.abstract.
43. P. Naylor, M. Lae, F. Reyal, and T. Walter, “Nuclei Segmentation in Histopathology Images Using Deep Neural Networks,” 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), pp. IEEE; EMB; IEEE Signal Proc Soc, 2017, doi: 10.1109/ISBI.2017.7950669.
44. M. Isokane et al., “ARHGEF17 is an essential spindle assembly checkpoint factor that targets Mps1 to kinetochores,” The Journal of Cell Biology, vol. 212, no. 6, pp. 647–659, Mar. 2016, doi: 10.1083/jcb.201408089.
45. V. Machairas, T. Baldeweck, T. Walter, and Etienne Décencière, “New General Features Based on Superpixels for image Segmentation Learning,” in International Symposium on Biomedical Imaging (ISBI): From Nano to Macro, 2016, pp. (accepted for publication).
46. N. Tsanov et al., “SmiFISH and FISH-quant - A flexible single RNA detection approach with super-resolution capability,” Nucleic Acids Research, vol. 44, no. 22, 2016, doi: 10.1093/nar/gkw784.
47. F. Eduati et al., “Prediction of human population responses to toxic compounds by a collaborative competition,” Nature Biotechnology, vol. 33, p. 933, Aug. 2015, [Online]. Available: https://doi.org/10.1038/nbt.3299.
48. V. Machairas, E. Decencière, and T. Walter, “Spatial Repulsion Between Markers Improves Watershed Performance BT - Mathematical Morphology and Its Applications to Signal and Image Processing,” 2015, pp. 194–202.
49. V. Machairas, M. Faessel, D. Cárdenas-peña, T. Chabardes, T. Walter, and E. Decencière, “Waterpixels,” IEEE Transactions on Image Processing, vol. 24, no. 11, pp. 3707–3716, 2015.
50. A. Schoenauer Sebag, S. Plancade, C. Raulet-Tomkiewicz, R. Barouki, J.-P. Vert, and T. Walter, “A generic methodological framework for studying single cell motility in high-throughput time-lapse data,” Bioinformatics, vol. 31, no. 12, pp. i320–i328, 2015, doi: 10.1093/bioinformatics/btv225.
51. A. Schoenauer Sebag, S. Plancade, C. Raulet-Tomkiewicz, R. Barouki, J.-P. Vert, and T. Walter, “Infering an ontology of single cell motions from high-throughput microscopy data,” in Proceedings of the 12th IEEE International Symposium on Biomedical Imaging (ISBI): From nano to macro, 2015, pp. 160–163.
52. J. Tegha-Dunghu et al., “MAP1S controls microtubule stability throughout the cell cycle in human cells,” Journal of Cell Science, vol. 127, no. 23, pp. 5007 LP–5013, Dec. 2014, doi: 10.1242/jcs.136457.
53. J. C. Costello et al., “A community effort to assess and improve drug sensitivity prediction algorithms.” Nature biotechnology, vol. 32, no. 12, pp. 1–103, 2014, doi: 10.1038/nbt.2877.
54. V. Graml et al., “A Genomic Multiprocess Survey of Machineries that control and Link Cell Shape, Microtubule Organization, and Cell-Cycle Progression,” Developmental Cell, vol. 31, no. 2, pp. 227–239, 2014, doi: 10.1016/j.devcel.2014.09.005.
55. J.-K. Hériché, J. G. Lees, I. Morilla, T. Walter, and B. Petrova, “Integration of biological data by kernels on graph nodes allows prediction of new genes involved in mitotic chromosome condensation,” Molecular Biology of the cell, vol. 25, pp. 2522–2536, 2014, doi: 10.1091/mbc.E13-04-0221.
56. V. Machairas, E. Decencière, and T. Walter, “Waterpixels: Superpixels based on the watershed transformation,” in International Conference on Image Processing (ICIP), 2014, pp. 4343–4347.
57. M. Veta et al., “Assessment of algorithms for mitosis detection in breast cancer histopathology images,” Medical Image Analysis, pp. 1–23, 2014.
58. G. Pau, T. Walter, B. Neumann, J. Hériché, J. Ellenberg, and W. Huber, “Dynamical modelling of phenotypes in a genome-wide RNAi live-cell imaging assay,” BMC bioinformatics, vol. 14, no. 308, pp. 1–10, 2013, doi: 10.1186/1471-2105-14-308.
59. M. Mall et al., “Mitotic lamin disassembly is triggered by lipid-mediated signaling.” The Journal of cell biology, vol. 198, no. 6, pp. 981–90, Sep. 2012, doi: 10.1083/jcb.201205103.
60. C. Conrad et al., “Micropilot: Automation of fluorescence microscopy-based imaging for systems biology.” Nature methods, vol. 8, no. 3, pp. 246–9, Mar. 2011, doi: 10.1038/nmeth.1558.
61. M. Held et al., “CellCognition: Time-resolved phenotype annotation in high-throughput live cell imaging.” Nature methods, vol. 7, no. 9, pp. 747–54, Sep. 2010, doi: 10.1038/nmeth.1486.
62. B. Dupas et al., “Evaluation of automated fundus photograph analysis algorithms for detecting microaneurysms, haemorrhages and exudates, and of a computer-assisted diagnostic system for grading diabetic retinopathy.” Diabetes & metabolism, vol. 36, no. 3, pp. 213–220, Jun. 2010, doi: 10.1016/j.diabet.2010.01.002.
63. T. Walter et al., “Phenotypic profiling of the human genome by time-lapse microscopy reveals cell division genes.” Nature, vol. 464, no. 7289, pp. 721–7, Apr. 2010, doi: 10.1038/nature08869.
64. T. Walter et al., “Automatic identification and clustering of chromosome phenotypes in a genome wide RNAi screen by time-lapse imaging.” Journal of structural biology, vol. 170, no. 1, pp. 1–9, Apr. 2010, doi: 10.1016/j.jsb.2009.10.004.
65. S. I. O. Donoghue et al., “Visualizing biological data — now and in the future,” Nature Methods, vol. 7, pp. S2–S4, 2010, doi: 10.1038/nmeth0310-S2.
66. S. Terjung, T. Walter, A. Seitz, B. Neumann, R. Pepperkok, and J. Ellenberg, “High-throughput microscopy using live mammalian cells.” in Live Cell Imaging: A Laboratory Manual, R. D. Goldman, J. R. Swedlow, and D. L. Spector, Eds. Cold Spring Harbor Laboratory Press, Cold Spring Harbor, 2010.
67. T. Walter et al., “Visualization of image data from cells to organisms,” Nature Methods, vol. 7, no. 3, pp. S26–S41, 2010, doi: 10.1038/NmEtH.1431.
68. J. Tegha-Dunghu et al., “EML3 is a nuclear microtubule-binding protein required for the correct alignment of chromosomes in metaphase,” Journal of Cell Science, vol. 121, no. 10, pp. 1718 LP–1726, May 2008, doi: 10.1242/jcs.019174.
69. T. Walter et al., “A genome wide RNAi screen by time lapse microscopy in order to identify mitotic genes - Computational aspects and challenges,” 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, ISBI, pp. 328–331, 2008, doi: 10.1109/ISBI.2008.4540999.
70. E. Denion, J.-R. Ordonez, J.-C. Klein, A. Glacet-Bernard, T. Walter, and G. Caputo, “Redistribution of the neurosensory retina in inferior limited macular translocation: An evaluation using image registration.” Graefe’s archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie, vol. 245, no. 3, pp. 437–442, Mar. 2007, doi: 10.1007/s00417-006-0408-1.
71. G. Sihn et al., “Anti-angiogenic properties of myo-inositol trispyrophosphate in ovo and growth reduction of implanted glioma.” FEBS letters, vol. 581, no. 5, pp. 962–6, Mar. 2007, doi: 10.1016/j.febslet.2007.01.079.
72. H. Erfle et al., “Reverse transfection on cell arrays for high content screening microscopy,” Nature Protocols, vol. 2, no. 2, pp. 392–399, Feb. 2007, doi: 10.1038/nprot.2006.483.
73. T. Walter, P. Massin, A. Erginay, R. Ordonez, C. Jeulin, and J.-C. Klein, “Automatic detection of microaneurysms in color fundus images.” Medical Image Analysis, vol. 11, no. 6, pp. 555–66, 2007.
74. T. Walter, R. Ordonez, and J.-C. Klein, “A morphological approach for skeleton filtering with reconstruction of the relevant branches,” presented at the 10th Computer Vision Winter Workshop, Feb. 2005.
75. T. Walter and J.-C. Klein, “Automatic Analysis of Color Fundus Photographs and Its Application to the Diagnosis of Diabetic Retinopathy BT - Handbook of Biomedical Image Analysis: Volume II: Segmentation Models Part B,” J. S. Suri, D. L. Wilson, and S. Laxminarayan, Eds. Boston, MA: Springer US, 2005, pp. 315–368.
76. T. Walter, “Application de la Morphologie Mathématique au diagnostic de la Rétinopathie Diabétique à partir d’images couleur,” PhD thesis, Mines ParisTech, Fontainebleau, France, 2003.
77. T. Walter and J.-C. Klein, “Automatic Detection of Microaneurysms in Color Fundus Images of the Human Retina by Means of the Bounding Box Closing,” 2002, vol. 2526, pp. 210–220.
78. T. Walter and J.-C. Klein, “A Computational Approach to Diagnosis of Diabetic Retinopathy,” in 6th Conference on Systemics, Cybernetics and Informatics (SCI), 2002, pp. 521–526.
79. T. Walter, J.-C. Klein, P. Massin, and A. Erginay, “A Contribution of Image Processing to the Diagnosis of Diabetic Retinopathy — Detection of Exudates in Color Fundus Images of the Human Retina,” IEEE Transactions on Medical Imaging, vol. 21, no. 10, pp. 1236–1243, 2002, doi: 10.1109/TMI.2002.806290.
80. T. Walter and J.-C. Klein, “Segmentation of Color Fundus Images of the Human Retina : Detection of the Optic Disc and the Vascular Tree Using Morphological Techniques,” 2001, vol. 2199, pp. 282–287, doi: 10.1007/3-540-45497-7_43.
81. T. Walter, J.-C. Klein, P. Massin, and F. Zana, “Automatic segmentation and registration of retinal fluorescein angiographies - Application to diabetic retinopathy,” presented at the First International Workshop on Computer Assisted Fundus Image Analysis (CAFIA), May 2000.
82. T. Walter, “Gewinnung von Merkmalen transitorisch evozierter otoakustischer Emissionen,” Diploma Thesis, Saarland university, Saarbrücken, Germany, 1999.