Thomas Walter

Professor

Center · CBIO

Discipline(s): Computer Science, Genetics, Genomics, Bioinformatics, Systems Biology, Imaging, Medical Technologies
Topic(s): Computer Science, Digital Twin, Genome and Cancer, Medical Image Analysis, Personalized and Predictive Medicine

Keywords

Awards & distinctions

2026 Best Poster Award IABM 2026 (Postdoc Matthieu Blons)
2025 1st and 2nd place in the international MICCAI challenge MIDOG (detection and classification of mitosis)
2025 Best poster award IABM 2025 (PhD student Alice Blondel)
2022 Best poster award IABM 2022 (PhD student Mélanie Lubrano)

Team

CBIO

Biography

Thomas Walter is a professor at Mines Paris – PSL, director of the Center for Computational Biology, and deputy director of the ‘Computational Oncology’ unit, jointly operated by the Institut Curie, Inserm, and Mines Paris. His research focuses on developing artificial intelligence, machine learning, and computer vision methods for image analysis in biology and medicine.

A significant part of his work concerns computational pathology. His team develops methods to predict, from digitized histopathology slides, the molecular characteristics of tumors, their prognosis, or their response to treatments. The goal is to better exploit data routinely produced in clinical settings and contribute to the development of more accessible precision medicine.

His research also addresses spatial transcriptomics and its integration with morphological and phenotypic data. These efforts aim to better segment and characterize cells within their tissue environment, link their morphology to gene expression, and better understand the spatial organization of tissues.

A third focus involves cell phenotyping from microscopy images, particularly dynamic data and high-content screening. This work enables the quantification of biological processes such as cell cycle and division, intracellular RNA localization, or the effects of drugs on cells. Before joining Mines Paris, Thomas Walter completed his PhD on computer-assisted diagnosis of diabetic retinopathy, followed by a postdoctoral position at EMBL focused on analyzing genomic screening through live-cell imaging.

Publication(s)

2026
MIPHEI-ViT: Multiplex immunofluorescence prediction from H&E images using ViT foundation models DOI : 10.1016/j.compbiomed.2026.111564
2026
sCellST predicts single-cell gene expression from H& E images DOI : 10.1038/s41467-025-67965-1
2026
Contrastive learning for cell division detection and tracking in live cell imaging data DOI : 10.1186/s12859-025-06344-5
2026
Self-supervised learning to predict intrahepatic cholangiocarcinoma transcriptomic classes on routine histology DOI : 10.1016/j.jhepr.2025.101675
2026
RNA2seg: a generalist model for cell segmentation in image-based spatial transcriptomics DOI : 10.1186/s13059-025-03908-9
2026
Deep learning analysis of breast cancer histology predicts ATM pathogenic variant carrier status DOI : 10.1038/s41746-026-02895-w
2026
autoFISH: a modular toolbox for sequential single-molecule RNA FISH experiments DOI : 10.1038/s42003-026-09979-z
2026
NeXtMarker: Contrastive Learning for Marker-Level Interpretability in Single-Cell Multiplex Imaging DOI : PMLR%20316:328-337
2026
Efficient Fine-Tuning of DINOv3 Pretrained on Natural Images for Atypical Mitotic Figure Classification DOI : 10.1007/978-3-032-25180-0_2
2026
Robust Pan-Cancer Mitotic Figure Detection with YOLOv12 DOI : 10.1007/978-3-032-25180-0_3
2025
Integration of clinical, pathological, radiological, and transcriptomic data improves prediction for first-line immunotherapy outcome in metastatic non-small cell lung cancer DOI : 10.1038/s41467-025-55847-5
2025
Exploring the risks of over-reliance on AI in diagnostic pathology. What lessons can be learned to support the training of young pathologists? DOI : 10.1371/journal.pone.0323270
2025
Cell-type deconvolution methods for spatial transcriptomics DOI : 10.1038/s41576-025-00845-y
2025
Identifying Patients With Low Relapse Rate Despite High-Risk Estrogen Receptor-Positive/Human Epidermal Growth Factor Receptor 2-Negative Early Breast Cancer: Development and Validation of a Clinicopathologic Assay DOI : 10.1200/JCO-25-00742
2025
A deep learning approach for time-consistent cell cycle phase prediction from microscopy data DOI : 10.1371/journal.pcbi.1013800
2025
Erratum: Identifying Patients With Low Relapse Rate Despite High-Risk Estrogen Receptor-Positive/Human Epidermal Growth Factor Receptor 2-Negative Early Breast Cancer: Development and Validation of a Clinicopathologic Assay DOI : 10.1200/JCO-25-02272
2025
InnoCube – Final design of the wireless satellite technology demonstration DOI : 10.1117/12.3062480
2024
Image-based multiplex immune profiling of cancer tissues: translational implications. A report of the International Immuno-oncology Biomarker Working Group on Breast Cancer DOI : 10.1002/path.6238
2024
A point cloud segmentation framework for image-based spatial transcriptomics DOI : 10.1038/s42003-024-06480-3
2024
TREM2-Expressing Multinucleated Giant Macrophages Are a Biomarker of Good Prognosis in Head and Neck Squamous Cell Carcinoma DOI : 10.1158/2159-8290.CD-24-0018
2024
Shedding light with harmonic radar: Unveiling the hidden impacts of streetlights on moth flight behavior DOI : 10.1073/pnas.2401215121
2024
pyHiM: a new open-source, multi-platform software package for spatial genomics based on multiplexed DNA-FISH imaging DOI : 10.1186/s13059-024-03178-x
2024
Cell-cycle-dependent mRNA localization in P-bodies DOI : 10.1016/j.molcel.2024.09.011
2024
Diagnosis with confidence: deep learning for reliable classification of laryngeal dysplasia DOI : 10.1111/his.15067
2023
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 DOI : 10.1002/path.6165
2023
Simple and Efficient Confidence Score for Grading Whole Slide Images
2023
Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer: a report of the international immuno-oncology biomarker working group DOI : 10.1002/path.6155
2023
Prediction of homologous recombination status with deep learning on breast cancer whole slide images; [Cancer du sein Utilisation de l’intelligence artificielle pour prédire le statut tumoral relatif à la recombinaison homologue] DOI : 10.1051/medsci/2023169
2023
An interactive murine single-cell atlas of the lung responses to radiation injury DOI : 10.1038/s41467-023-38134-z
2023
Automatic Grading of Cervical Biopsies by Combining Full and Self-supervision DOI : 10.1007/978-3-031-25082-8_27
2023
Giga-SSL: Self-Supervised Learning for Gigapixel Images DOI : 10.1109/CVPRW59228.2023.00453
2023
PointFISH: Learning Point Cloud Representations for RNA Localization Patterns DOI : 10.1007/978-3-031-25069-9_32
2023
HT-smFISH: a cost-effective and flexible workflow for high-throughput single-molecule RNA imaging DOI : 10.1038/s41596-022-00750-2
2023
Learning with Minimal Effort: Leveraging in Silico Labeling for Cell and Nucleus Segmentation DOI : 10.1007/978-3-031-25069-9_28
2023
Cell Segmentation in Images Without Structural Fluorescent Labels DOI : 10.1017/S2633903X23000168
2022
Deep learning identifies morphological patterns of homologous recombination deficiency in luminal breast cancers from whole slide images DOI : 10.1016/j.xcrm.2022.100872
2022
FISH-quant v2: a scalable and modular tool for smFISH image analysis DOI : 10.1261/rna.079073.121
2022
Neural network fast-classifies biological images through features selecting to power automated microscopy DOI : 10.1111/jmi.13062
2022
Prediction of Treatment Response in Triple Negative Breast Cancer From Whole Slide Images DOI : 10.3389/frsip.2022.851809
2021
A choreography of centrosomal mRNAs reveals a conserved localization mechanism involving active polysome transport DOI : 10.1038/s41467-021-21585-7
2021
Innocube—a wireless satellite platform to demonstrate innovative technologies DOI : 10.3390/aerospace8050127
2021
Spatial transcriptomics for respiratory research and medicine DOI : 10.1183/13993003.04314-2020
2021
Which image-based phenotypes are most promising for using AI to understand cellular functions and why? DOI : 10.1016/j.cels.2021.04.012
2021
A new innovative real-time tracking method for flying insects applicable under natural conditions DOI : 10.1186/s40850-021-00097-3
2021
The kinesin KIF1C transports APC-dependent mRNAs to cell protrusions DOI : 10.1261/rna.078576.120
2020
Experimentally-Generated Ground Truth for Detecting Cell Types in an Image-Based Immunotherapy Screen DOI : 10.1109/ISBI45749.2020.9098696
2020
Domain-invariant features for mechanism of action prediction in a multi-cell-line drug screen DOI : 10.1093/bioinformatics/btz774
2020
A Dual Protein-mRNA Localization Screen Reveals Compartmentalized Translation and Widespread Co-translational RNA Targeting DOI : 10.1016/j.devcel.2020.07.010
2019
Wireless local positioning system for controlled UAV landing in GNSS-denied environment DOI : 10.1109/MetroAeroSpace.2019.8869587
2019
Segmentation of Nuclei in Histopathology Images by Deep Regression of the Distance Map DOI : 10.1109/TMI.2018.2865709
2019
Human lymphoid organ cDC2 and macrophages play complementary roles in T follicular helper responses DOI : 10.1084/jem.20181994
2019
Predicting residual cancer burden in a triple negative breast cancer cohort DOI : 10.1109/ISBI.2019.8759205
2019
A deep learning approach to identify MRNA localization patterns DOI : 10.1109/ISBI.2019.8759235
2018
A computational framework to study sub-cellular RNA localization DOI : 10.1038/s41467-018-06868-w
2018
Analysing double-strand breaks in cultured cells for drug screening applications by causal inference DOI : 10.1109/ISBI.2018.8363612
2018
Ultra-wideband based pose estimation for small unmanned aerial vehicles DOI : 10.1109/ACCESS.2018.2873571
2017
Kernel Multitask Regression for Toxicogenetics DOI : 10.1002/minf.201700053
2017
Nuclei segmentation in histopathology images using deep neural networks DOI : 10.1109/ISBI.2017.7950669
2017
Power distribution unit (PDU) for a distributed computing network DOI : 10.23919/FRUCT.2017.8250171
2016
SmiFISH and FISH-quant - A flexible single RNA detection approach with super-resolution capability DOI : 10.1093/nar/gkw784
2016
New general features based on superpixels for image segmentation learning DOI : 10.1109/ISBI.2016.7493531
2016
ARH GEF17 is an essential spindle assembly checkpoint factor that targets Mps1 to kinetochores DOI : 10.1083/jcb.201408089
2015
A generic methodological framework for studying single cell motility in high-throughput time-lapse data DOI : 10.1093/bioinformatics/btv225
2015
Infering an ontology of single cell motions from high-throughput microscopy data DOI : 10.1109/ISBI.2015.7163840
2015
Prediction of human population responses to toxic compounds by a collaborative competition DOI : 10.1038/nbt.3299
2015
Assessment of algorithms for mitosis detection in breast cancer histopathology images DOI : 10.1016/j.media.2014.11.010
2015
Spatial repulsion between markers improves watershed performance DOI : 10.1007/978-3-319-18720-4_17
2015
Waterpixels DOI : 10.1109/TIP.2015.2451011
2014
A community effort to assess and improve drug sensitivity prediction algorithms DOI : 10.1038/nbt.2877
2014
MAP1S controls microtubule stability throughout the cell cycle in human cells DOI : 10.1242/jcs.136457
2014
Integration of biological data by kernels on graph nodes allows prediction of new genes involved in mitotic chromosome condensation DOI : 10.1091/mbc.E13-04-0221
2014
Waterpixels: Superpixels based on the watershed transformation DOI : 10.1109/ICIP.2014.7025882
2014
A genomic multiprocess survey of machineries that control and link cell shape, microtubule organization, and cell-cycle progression DOI : 10.1016/j.devcel.2014.09.005
2013
Dynamical modelling of phenotypes in a genome-wide RNAi live-cell imaging assay DOI : 10.1186/1471-2105-14-308
2012
Wireless avionics for a solar sailor (GOSSAMER-1)
2012
Mitotic lamin disassembly is triggered by lipid-mediated signaling DOI : 10.1083/jcb.201205103
2011
Micropilot: Automation of fluorescence microscopy-based imaging for systems biology DOI : 10.1038/nmeth.1558
2010
Visualizing biological data—now and in the future DOI : 10.1038/nmeth.f.301
2010
Erratum: Visualization of image data from cells to organisms (Nature Methods (2010) 7 (S26-S41)) DOI : 10.1038/nmeth0610-479a
2010
Visualization of image data from cells to organisms DOI : 10.1038/nmeth.1431
2010
High throughput microscopy: On the scent of cell division genes; [Hochdurchsatzmikroskopie zellteilungsgenen auf der spur]
2010
High-throughput microscopy using live mammalian cells DOI : 10.1101/pdb.top84
2010
Phenotypic profiling of the human genome by time-lapse microscopy reveals cell division genes DOI : 10.1038/nature08869
2010
Automatic identification and clustering of chromosome phenotypes in a genome wide RNAi screen by time-lapse imaging DOI : 10.1016/j.jsb.2009.10.004
2010
CellCognition: Time-resolved phenotype annotation in high-throughput live cell imaging DOI : 10.1038/nmeth.1486
2010
Evaluation of automated fundus photograph analysis algorithms for detecting microaneurysms, haemorrhages and exudates, and of a computer-assisted diagnostic system for grading diabetic retinopathy DOI : 10.1016/j.diabet.2010.01.002
2008
EML3 is a nuclear microtubule-binding protein required for the correct alignment of chromosomes in metaphase DOI : 10.1242/jcs.019174
2008
A genome wide RNAi screen by time lapse microscopy in order to identify mitotic genes - Computational aspects and challenges DOI : 10.1109/ISBI.2008.4540999
2007
Redistribution of the neurosensory retina in inferior limited macular translocation: An evaluation using image registration DOI : 10.1007/s00417-006-0408-1
2007
Anti-angiogenic properties of myo-inositol trispyrophosphate in ovo and growth reduction of implanted glioma DOI : 10.1016/j.febslet.2007.01.079
2007
Reverse transfection on cell arrays for high content screening microscopy DOI : 10.1038/nprot.2006.483
2007
Automatic detection of microaneurysms in color fundus images DOI : 10.1016/j.media.2007.05.001
2005
Automatic Analysis of Color Fundus Photographs and Its Application to the Diagnosis of Diabetic Retinopathy DOI : 10.1007/0-306-48606-7_7
2002
A contribution of image processing to the diagnosis of diabetic retinopathy - Detection of exudates in color fundus images of the human retina DOI : 10.1109/TMI.2002.806290
2002
Automatic detection of microaneurysms in color fundus images of the human retina by means of the bounding box closing DOI : 10.1007/3-540-36104-9_23
2001
Segmentation of color fundus images of the human retina: Detection of the optic disc and the vascular tree using morphological techniques DOI : 10.1007/3-540-45497-7_43

Projects

2023-2030 Cell-ID Partner Le Programme de recherche exploratoire Identités et destins cellulaires (PEPR Cell-ID) est financé par France 2030. Son objectif est de déployer la médecine d’interception dans le domaine de la recherche sur les cancers pédiatriques du cerveau.
2019-2024 PRAIRIE Partner Prairie (for PaRis Artificial Intelligence Research InstitutE) is an institute for interdisciplinary research and education in artificial intelligence (AI) with an international ambition. Located in central Paris, it is anchored in the tremendous strengths of French AI available there, as embodied by our academic partners, CNRS, Inria, PSL University, and Université de Paris, that excel in both core methodological areas of AI, its interdisciplinary aspects at the frontier between computer science and applied mathematics with biology, cognitive science, and physics, and high-impact applications in health and medicine.
2024-2030 PRAIRIE-PSAI Partner PR[AI]RIE has the ambitious objective of becoming a world leader in research and higher education in AI, with a real impact on the economy and society, driven by its founding members, world class academic institutions, industrial partners leading in their fields and a network of strong international partners.
2019-2023 Fibrogenèse pulmonaire: approche systémique par transcriptomique spatiale (ANR) Partner La fibrose pulmonaire est une maladie chronique et progressive caractérisée par la destruction du tissu pulmonaire et son remplacement par du tissu cicatriciel. Les échanges gazeux au niveau alvéolaire se trouvent ainsi interrompus conduisant à la défaillance de l'organe et la mort du patient. Les mécanismes physiopathologiques de la fibrose pulmonaire restent mal connus bien que des processus complexes comme la transformation des fibroblastes, la présence d'un microenvironnement pro-inflammatoire chronique ainsi qu'un rôle important de la sénescence aient été évoqués. L'objectif de ce projet était d'étudier dans un modèle murin de fibrose pulmonaire radio-induite les conséquences moléculaires et cellulaires de l'irradiation sur le compartiment alvéolaire, l'unité fonctionnelle du poumon.
2014-2018 Analyse systématique de l'expression des gènes dans des cellules isolées et intactes (ANR) Partner L'objectif de ce projet était d'analyser de façon systématique la localisation d'ARN dans des cellules.
2019-2023 Découverte et analyse des usines de traduction : de nouvelles structures qui compartimentent finement le cytoplasme (ANR) Partner Le projet TRANSFACT étudie des structures cellulaires uniques appelées « usines de traduction » (translation factories). Ce sont des zones spéciales dans le cytoplasme de la cellule qui regroupent les molécules nécessaires pour fabriquer de nouvelles protéines.
2022-2025 Prédiction de la survie chez les patients avec un cancer de la vessie traités par chimiothérapie néoadjuvante avant cystectomie Partner Ce projet de recherche clinique vise à identifier des indicateurs biologiques, génétiques ou radiologiques précis afin d'anticiper les chances de guérison des patients atteints d'un cancer de la vessie agressif. Plus précisément, il étudie l'efficacité de la chimiothérapie administrée avant l'ablation chirurgicale de la vessie (la cystectomie). L'objectif principal est de réussir à personnaliser la prise en charge médicale en prédisant à l'avance quels patients répondront le mieux à ce traitement lourd.
2024-2028 Models for predicting treatment response using integrative analysis of clinical, molecular and imaging data in a large follicular lymphoma cohort (INSERM MESSIDORE) Ce projet s'appuie sur une grande cohorte de patients atteints d'un lymphome folliculaire (un cancer du sang indolent). L'objectif est de développer des modèles mathématiques et d'intelligence artificielle capables de prédire la réponse aux traitements. Pour y parvenir, les chercheurs croisent de manière inédite et intégrative les données cliniques des patients, leurs analyses moléculaires (génétiques) et leurs examens d'imagerie médicale (radiomique).

Teaching

Deep Learning for Image Analysis

2018 – en cours Lecturer

The course will consist of lectures and hands-on sessions. The lectures will cover: - Introduction to machine learning. - Artificial neural networks, backpropagation algorithm - Convolutional neural networks - Successful architectures - Analysis of neural network functionality - Image classification and segmentation - Autoencoders and generative networks - Current trends and research prospects During the practical sessions, students will implement the concepts learned in class using Python. They will tackle practical problems related to deep learning: architecture design, optimization strategies, hyperparameter selection, and analysis of results.

R_Santé. Deep Learning: Applications in Biology and Medicine (Computational Pathology)

2020 – en cours Lecturer

Deep Learning for Image Analysis: Applications in Biology and Medecine (M2 Cancerology)

2021 – en cours Lecturer

Introduction à l'analyse d'images pour des biologistes / médecins dans le cadre d'un master en cancerologie

Fondations du Machine Learning

2022 – en cours Lecturer

Un cours sur les bases du Machine Learning pour des partenaires industriels. Cours dans le cadre de la formation continue proposée par EXED.

Deep Learning for Image Analysis (M2 IASD)

2020 – en cours Lecturer

Un cours M2 (Master PSL IASD) sur l'analyse d'images. - réseaux convolutifs - transformers - visualisation - auto-supervision - optimisation - architectures - applications : détection d'objets, segmentation, classification

Biomedical Image Analysis (M2)

2025 – en cours Course Director

Ce cours donne un panorama des méthodes en analyse d'images pour le domain biomédical, notamment en pathologie computationnelle, analyse d'images en microscopie, et la radiomique.

PhD supervision

2024 Prediction of homologous recombination deficiency as a pan-cancer biomarker Raphaël BOURGADE
2023 Computational methods for histopathology images and spatial transcriptomics data Alice BLONDEL
2023 Machine learning-based estimation of molecular biomarkers from H&E images: performance characterization, limitations, and downstream applications Guillaume BALEZO
2023 Approches d'apprentissage automatique pour l'analyse de données d'images et d'omiques appliqués au diagnostic du cancer Luca GORTANA
2022 Computer vision for phenotypic profiling of the cell cycle Thomas BONTE
2021 Development of computational methods for the analysis of spatial transcriptomic imaging data Thomas DEFARD
2021 Faire le lien entre morphologie cellulaire et transcriptomique grâce à l’apprentissage automatique Loïc CHADOUTAUD
2020 Deep learning methods to improve the diagnosis of laryngeal squamous cell carcinoma from histological images Mélanie LUBRANO DI SCANDALEA
2020 A Versatile Single-Cell Analysis Pipeline for High-Content Video-Microscopy Screening Daniel ZYSS
2019 Computational pathology representation learning: application to predicting cancer molecular features Tristan LAZARD
2018 Computer vision for analyzing intracellular RNA localization patterns Arthur IMBERT-MEISSIREL
2016 Deep learning for computational phenotyping in cell-based assays Joseph BOYD
2015 From cellular phenotyping to digital slide classification: An application to triple-negative breast cancer treatment Peter NAYLOR
2014 Development of a statistical analysis framework for sub-cellular mRNA localization Aubin SAMACOITS
2013 Waterpixels and their application to image segmentation learning Vaïa MACHAIRAS
2012 The versatility of high-content high-throughput time-lapse screening data : developing generic methods for data re-use and comparative analyses Alice SCHOENAUER SEBAG