Hrvoje Bogunović
278 posts
Hrvoje Bogunović
@hbogunovic
Researcher in Machine Learning for Medical Imaging. Applications in Ophthalmology. Works @MedUni_Wien
Vienna, Austria Katılım Mayıs 2009
860 Takip Edilen310 Takipçiler
Extremely grateful to @ERC_Research for awarding me the #ERCCoG grant and this unique opportunity!
The project HealthAEye will pursue AI-first approaches to retinal imaging to enable home-based monitoring using portable devices.
So thankful to my colleagues and mentors!
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@hbogunovic @ERC_Research This is fantastic news, congratulations Hrvoje! Looking forward to seeing more of your exciting HealthAEye project 🚀
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So many people helped me along the way. I feel my entire scientific career contributed to this from the first scientific steps at @fer_unizg, over my PhD at @upf to the specialization in AI for retinal imaging at @uiowa and @MedUni_Wien/@OptimaLab
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Happy to see published this extensive analysis of CLIP and CLOOB techniques for learning effective multimodal and multidimensional Fundus-OCT representations. Led by amazing @EmeseSukei in a great collaboration with @gklambauer and his JKU team.
Emese Sükei@EmeseSukei
🚀 Published in @SciReports: Our multimodal #ContrastiveLearning study uses 2D & 3D retinal images to enhance prediction accuracy in #Ophthalmology. It also shows that lower-cost fundus imaging can still deliver clinical insights. Learn more! 👀🔍 🔗rdcu.be/dZa2N
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Happy that our group was able to attend and present multiple works at the inspiring #MICCAI2024 in sunny Marrakech
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We showcase this on the task of predicting age-related macular degeneration (AMD) but we expect the approach to apply generally to survival analysis tasks in the medical domain.
If interested in deep learning for disease progression modeling stop by his talk/poster on Wednesday.
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Furthermore, we also incorporate intra-subject consistency by requiring the NeuralODE estimates of the feature and risk at future time-points to be consistent with the values obtained using the actual scan of the future visit.
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Excited to be attending #MIDL2024. Our lab will be presenting the work led by our star postdoc Arunava Chakravarty on learning to predict the risk of disease progression from longitudinal data.
Paper: openreview.net/pdf?id=x7EqWCy…
His talk/poster is on Wednesday.
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The obtained representations achieved state-of-the-art results in predicting the conversion from intermediate to late-stage age-related macular degeneration (AMD) from retinal OCT scans.
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The paper introduces a learnable temporal equivariance module in a contrastive pretraining setup. The proposed module enables direct manipulation of the representation space by generating future representations.
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Excited to share the work led by our exceptional PhD student Taha Emre on the representation learning method for longitudinal imaging data that got an early acceptance (top 11%) at #MICCAI2024
Preprint: arxiv.org/abs/2405.09404
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@gklambauer Awesome news! Big congratulations so well deserved
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We are very happy to receive a big grant of 33M€ for developing Artificial Intelligence methods in the next five years!!
JKU Linz teams up with TU Vienna, IST Austria, TU Graz, AAU Klagenfurt und WU Vienna.
We'll make it worth!
FWF@FWF_at
@UniGraz @MedUniGraz @MedUni_Wien 💡„Bilateral Artificial Intelligence“ - Die neue Dimension der KI entdecken Wir gratulieren! 👏 #exzellenzinitiative #spitzenforschung @jkulinz @tugraz @wu_vienna @ISTAustria @tu_wien
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Yeah, so this thing happened and ever since people call me doctor…
This wouldn’t have been possible without the people around me, especially @gklambauer .
Thank you!
Günter Klambauer@gklambauer
Happy to share the PhD thesis of @ml_hoedt : "On signal propagation theory in neural networks" epub.jku.at/obvulihs/conte…
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Happy to report our publication in MedIA led by brilliant Philipp Seeböck and @ignaciorlando , which demonstrates a simple yet effective way to boost the performance of biomarker segmentation networks on retinal OCT by including anomaly detection maps. authors.elsevier.com/c/1ia~X_UzlO1o…
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Really nice exercise on explainable AI. Looking forward to the results.
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Last of #MICCAI2023 but not least, two face-to-face posters on forecasting from OCT volumes by Taha and Marzieh and adapting SAM for OCT segmentation by Botond. arxiv.org/abs/2308.09331
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José from our @OptimaLab is ready to present his #MICCAI2023 work on label-efficient 3D-to-2D segmentation. arxiv.org/abs/2307.03008
Stop by W-05-xxx
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