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Journal Article

  1. [VIND-23a]“Guided Deep Embedded Clustering regularization for multi-feature medical signal classification”, Pattern Recognition, vol. 143, 2023.PDF icon Vindas-23_PR_Guided deep embedded clustering regularization for multifeature classif.pdf (3.01 MB)
  2. [VIND-22a]“Semi-automatic data annotation based on feature-space projection and local quality metrics: an application to cerebral emboli characterization”, Medical Image Analysis, 2022.PDF icon Vindas-22_Media_Semi_supervised_Data_Annotation.pdf (13.46 MB)
  3. [GUEP-18]“Sequential Emboli Detection from Ultrasound Outpatient Data”, IEEE Journal of Biomedical and Health Informatics, 2018.PDF icon Guepie-18 jbhi Sequential Emboli Detection from Ultrasound Outpatient Data.pdf (1.66 MB)
  4. [GUEP-17]“Discrimination between emboli and artifacts for outpatient transcranial Doppler ultrasound data”, Medical & Biological Engineering & Computing, pp. 1-11, 2017.PDF icon Medical_&_Biological_Engineering_&_Computing_2016.pdf (1.54 MB)

Conference Paper

  1. [VIND-23c]“Deep Embedded Clustering regularization for supervised imbalanced cerebral emboli classification using transcranial Doppler ultrasound”, in 31st European Signal Processing Conference (EUSIPCO 2023), september 4-8, Helsinki, Finland, 2023.PDF icon Vindas-23_Eusipco_DEC regularization for supervised imbalanced cerebral emboli classification using TDC.pdf (392.52 KB)
  2. [VIND-23b]“Soft-labels noise tolerant loss functions for transcranial Doppler ultrasound signal classification”, in IEEE Ultrasonics symposium, 2023, pp. 1-4.PDF icon Vindas-23_IEEE-IUS_Soft labels noise tolerant loss functions for TCD signal classification.pdf (632.81 KB)
  3. [VIND-22b]“An hybrid CNN-Transformer model based on multi-feature extraction and attention fusion mechanism for classification of medical signals”, in Machine Learning for Healthcare, 2022.
    https://static1.squarespace.com/static/59d5ac1780bd5ef9c396eda6/t/62e97636b9962e1079555986/1659467321416/43+Paper-43_CameraReady.pdf
  4. [VIND-21]“Semi-supervised annotation of Transcranial Doppler ultrasound micro-embolic data”, in IEEE International Ultrasonics Symposium, 2021.PDF icon Vindas-21_IUS_Semi-supervised annotation of Transcranial Doppler.pdf (1.84 MB)
  5. [DELA-20]“Automatic classification of brain emboli using transcranial Doppler and convolutional neural networks”, in Forum Acusticum, Dec 2020, Lyon, France, 2020, pp. 2693-2694.
    https://hal.science/hal-03242473/document
  6. [GUEP-15b]B. K. Guépié, Sciolla, B., Millioz, F., Ramassamy, R., Pinatel, E., Almar, M., Guibert, B., and Delachartre, P., “Time-frequency Blood Flow Parameters Estimation for Micro-emboli Detection”, in IEEE ISPA , Zagreb, Croatie, 2015.PDF icon article_ipsa2015.pdf (1.24 MB)
  7. [GUEP-15a]B. K. Guépié, Almar, M., Guibert, B., and Delachartre, P., “Détection de micro-emboles par une approche de classification supervisée”, in GRETSI, France, 2015.PDF icon guepie_gretsi2015.pdf (63.84 KB)
  8. [LIN-14b]F. Lin, Saglio, C., Almar, M., Guibert, B., and Delachartre, P., “Micro-embolic signal characterization based on long time Doppler time-frequency image processing and statistics”, in IEEE International Conference on Signal Processing, Hangzhou, China, 2014, pp. 1046-1051.