Gevaert, Olivier

Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation

Malta, T. M., Sokolov, A., Gentles, A. J., Burzykowski, T., Poisson, L., Weinstein, J. N., Kamińska, B., Huelsken, J., Omberg, L., Gevaert, O., et al. (2018) · Cell

Cancer Cells and MetastasisPluripotent Stem Cells Research

Cited by 2,180

Disparities in dermatology AI performance on a diverse, curated clinical image set

Daneshjou, R., Vodrahalli, K., Novoa, R. A., Jenkins, M., Liang, W., Rotemberg, V., Ko, J., Swetter, S. M., Bailey, E. E., Gevaert, O., et al. (2022) · Science Advances

Cutaneous Melanoma Detection and ManagementArtificial Intelligence in Healthcare and Education

Cited by 544

Deep learning with multimodal representation for pancancer prognosis prediction

Cheerla, A., & Gevaert, O. (2019) · Bioinformatics

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 421

Multimodal data fusion for cancer biomarker discovery with deep learning

Steyaert, S., Pizurica, M., Nagaraj, D., Khandelwal, P., Hernandez-Boussard, T., Gentles, A. J., & Gevaert, O. (2023) · Nature Machine Intelligence

Radiomics and Machine Learning in Medical ImagingMachine Learning in Healthcare

Cited by 402

Glioblastoma Multiforme: Exploratory Radiogenomic Analysis by Using Quantitative Image Features

Gevaert, O., Mitchell, L. A., Achrol, A. S., Xu, J., Echegaray, S., Steinberg, G. K., Cheshier, S. H., Napel, S., Zaharchuk, G., & Plevritis, S. K. (2014) · Radiology

Radiomics and Machine Learning in Medical ImagingGlioma Diagnosis and Treatment

Cited by 319

Foundation metrics for evaluating effectiveness of healthcare conversations powered by generative AI

Abbasian, M., Khatibi, E., Azimi, I., Oniani, D., Shakeri Hossein Abad, Z., Thieme, A., Sriram, R., Yang, Z., Wang, Y., Lin, B., et al. (2024) · npj Digital Medicine

Artificial Intelligence in Healthcare and EducationArtificial Intelligence in Healthcare

Cited by 213

An expanded universe of cancer targets

Hahn, W. C., Bader, J. S., Braun, T. P., Califano, A., Clemons, P. A., Druker, B. J., Ewald, A. J., Fu, H., Jagu, S., Kemp, C. J., et al. (2021) · Cell

Cancer Genomics and DiagnosticsChromatin Remodeling and Cancer

Cited by 213

Evaluation of a panel of 28 biomarkers for the non-invasive diagnosis of endometriosis

Vodolazkaia, A., El-Aalamat, Y., Popovic, D., Mihalyi, A., Bossuyt, X., Kyama, C. M., Fassbender, A., Bokor, A., Schols, D., Huskens, D., et al. (2012) · Human Reproduction

Endometriosis Research and TreatmentEctopic Pregnancy Diagnosis and Management

Cited by 189

Non-invasive diagnosis of endometriosis based on a combined analysis of six plasma biomarkers

Mihalyi, A., Gevaert, O., Kyama, C. M., Simsa, P., Pochet, N., De Smet, F., De Moor, B., Meuleman, C., Billen, J., Blanckaert, N., et al. (2010) · Human Reproduction

Endometriosis Research and TreatmentEctopic Pregnancy Diagnosis and Management

Cited by 161

Predictive radiogenomics modeling of EGFR mutation status in lung cancer

Gevaert, O., Echegaray, S., Khuong, A., Hoang, C. D., Shrager, J. B., Jensen, K. C., Berry, G. J., Guo, H. H., Lau, C., Plevritis, S. K., et al. (2017) · Scientific Reports

Radiomics and Machine Learning in Medical ImagingLung Cancer Diagnosis and Treatment

Cited by 160

Density of small diameter sensory nerve fibres in endometrium: a semi-invasive diagnostic test for minimal to mild endometriosis

Bokor, A., Kyama, C. M., Vercruysse, L., Fassbender, A., Gevaert, O., Vodolazkaia, A., De Moor, B., Fulop, V., & D'Hooghe, T. (2009) · Human Reproduction

Endometriosis Research and TreatmentGynecological conditions and treatments

Cited by 128

A meta-learning approach for genomic survival analysis

Qiu, Y. L., Zheng, H., Devos, A., Selby, H., & Gevaert, O. (2020) · Nature Communications

Gene expression and cancer classificationDomain Adaptation and Few-Shot Learning

Cited by 96

Combined mRNA microarray and proteomic analysis of eutopic endometrium of women with and without endometriosis

Fassbender, A., Verbeeck, N., Börnigen, D., Kyama, C. M., Bokor, A., Vodolazkaia, A., Peeraer, K., Tomassetti, C., Meuleman, C., Gevaert, O., et al. (2012) · Human Reproduction

Endometriosis Research and TreatmentEctopic Pregnancy Diagnosis and Management

Cited by 87

Proteomics Analysis of Plasma for Early Diagnosis of Endometriosis

Fassbender, A., Waelkens, E., Verbeeck, N., Kyama, C. M., Bokor, A., Vodolazkaia, A., Van de Plas, R., Meuleman, C., Peeraer, K., Tomassetti, C., et al. (2012) · Obstetrics & Gynecology

Endometriosis Research and TreatmentAdvanced Proteomics Techniques and Applications

Cited by 51

The ENGAGE study: Integrating neuroimaging, virtual reality and smartphone sensing to understand self-regulation for managing depression and obesity in a precision medicine model

Williams, L. M., Pines, A., Goldstein-Piekarski, A. N., Rosas, L. G., Kullar, M., Sacchet, M. D., Gevaert, O., Bailenson, J., Lavori, P. W., Dagum, P., et al. (2018) · Behaviour Research and Therapy

Behavioral Health and InterventionsNeural and Behavioral Psychology Studies

Cited by 40

Generation of synthetic whole-slide image tiles of tumours from RNA-sequencing data via cascaded diffusion models

Carrillo-Perez, F., Pizurica, M., Zheng, Y., Nandi, T. N., Madduri, R., Shen, J., & Gevaert, O. (2024) · Nature Biomedical Engineering

AI in cancer detectionSingle-cell and spatial transcriptomics

Cited by 37

TRIzol treatment of secretory phase endometrium allows combined proteomic and mRNA microarray analysis of the same sample in women with and without endometriosis

Fassbender, A., Simsa, P., Kyama, C. M., Waelkens, E., Mihalyi, A., Meuleman, C., Gevaert, O., Van de Plas, R., de Moor, B., & D'Hooghe, T. M. (2010) · Reproductive Biology and Endocrinology

Endometriosis Research and TreatmentAdvanced Proteomics Techniques and Applications

Cited by 23

Meta-learning reduces the amount of data needed to build AI models in oncology

Gevaert, O. (2021) · British Journal of Cancer

Domain Adaptation and Few-Shot LearningArtificial Intelligence in Healthcare and Education

Cited by 13

Dropout-Enabled Ensemble Learning for Multi-scale Biomedical Data

Momeni, A., Thibault, M., & Gevaert, O. (2019) · Lecture Notes in Computer Science

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 12