Benjamin Lambert @benolmbrt
PhD in Deep Learning. My main research topic is uncertainty quantification for medical image segmentation. benolmbrt.github.io Rennes Joined April 2021-
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“Robust Conformal Volume Estimation in 3D Medical Images“ #conformalprediction
Our Trustworthy AI in Medical Imaging book from the MICCAI/Elsevier series is now online! We want to thank all the authors for their valuable contributions that made this work possible. For more information, please visit the book’s page: shop.elsevier.com/books/trustwor…
A wonderful conclusion to these 3 years of thesis. 🎉 Thank you kindly to the jury members for the exciting discussions about the challenges of AI in clinical routine 🏥 @NinonBurgos @marazul @c_f_baumgartner @julienmairal @fran_galassi Olivier François & Pierrick Coupé
A wonderful conclusion to these 3 years of thesis. 🎉 Thank you kindly to the jury members for the exciting discussions about the challenges of AI in clinical routine 🏥 @NinonBurgos @marazul @c_f_baumgartner @julienmairal @fran_galassi Olivier François & Pierrick Coupé
Thrilled to announce that our paper "Robust Conformal Volume Estimation in 3D Medical Images" has been early accepted at #MICCAI2024🎉 A beautiful conclusion to my PhD journey, before my defense this Thursday. Stay tuned for more info! @nifm_gin @InriaStatify @pixylmedical
📢PhD defense on 16/05 at 10am! 🎓 🤖How can we guarantee the safe deployment of deep learning models in medical imaging? 🗣️Benjamin Lambert dives into this issue with his thesis: "#Uncertainty Quantification in Deep Learning-based Medical Image segmentation". #AI #MRI #medicalAI
Interested in trustworthy AI for medical applications? see sciencedirect.com/science/articl…
New didactic review: Preventing dataset shift from breaking machine-learning biomarkers academic.oup.com/gigascience/ar… Big picture on dataset shift in machine learning for non-specialists. Intuitions on why handling "nuisances" in machine learning differs from standard statistics. 1/7
🎓 Congratulations to Dr. Lucrezia Carboni for successfully defending her PhD! 🌟 She presented her research on "GRAAACE: GRAphs for Artificial neural networks and brAin Connectivity Exploration" with remarkable clarity. 🧠📚 #GraphTheory #NeuralNetworks #BrainConnectivity
AI in Medicine is suffering from what I would like to call "The Cassandra Problem" 🧵(1/17)
Presenting TriadNet today at 14h15 at the @UNSURE_Workshop ! There will also be a poster session at lunch time. If you are curious about how enhancing your volume or area estimation with predictive intervals, feel free to join🔥 #MICCAI2023
Presenting TriadNet today at 14h15 at the @UNSURE_Workshop ! There will also be a poster session at lunch time. If you are curious about how enhancing your volume or area estimation with predictive intervals, feel free to join🔥 #MICCAI2023
⚠️A widespread confusion: calibration of predictors, as measured by expected calibration error, does not control completely that the predictor gives perfect probabilities P(y|X): A predictor may be overconfident on some individuals and underconfident on others 🧵 1/10
Au #gretsi2023 aujourd'hui pour présenter Triade Net, notre approche simple et interprétable pour obtenir des intervalles de confiance sur les volumes et superficies (avec garanties statistiques !)
✨ Preprint: Confidence intervals for performance estimates in 3D medical image segmentation arxiv.org/abs/2307.10926 Studying evaluation uncertainty of image segmentation shows 🔶good Gaussian approximation with task-specific variance 🔶less images needed than for classification
Attending the @ISMRM in Toronto ? Interested in QC for brain MRI ?🧠 ➡️Meet @Thomas__Coudert who will be presenting our poster #3309 "Uncertainty-based Quality Control for Subcortical Structures Segmentation in T1-weighted Brain MRI" 📍Tuesday, 06 June 23, 15:45, Computer 79

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