@AI4AD

The Artificial Intelligence for Alzheimer’s Disease Consortium - empowering discovery through machine learning in AD neuroimaging, genetics and cognition

 Los Angeles, CA
ai4ad.org/
Joined October 2020
  • Tweets64
  • Following122
  • Followers182
3Media
AI4AD Consortium retweeted
Jason H. Moore, PhD@moorejh1 Apr 2024
We are recruiting a Chief Genomics Officer here at Cedars-Sinai Medical Center in Los Angeles. Please spread the word! Apply here: apply.interfolio.com/142316 #genomics #genetics
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AI4AD Consortium retweeted
Li Shen@lishenlc24 Feb 2024
Our latest paper by Zexuan and Qipeng (@PennAMCS) and Boning (@pennbioeng) introduces the Distance-Weighted Sinkhorn (DWS) Loss for enhancing classification performance: cell.com/iscience/fulltext/S…. #AI #AlzheimersResearch #DataScience @UPennDBEI @UPennIBI @PennAI2D @AI4AD
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Our AI4AD Consortium in @Nature Magazine's article by @maxdkozlov 👉 nature.com/articles/d41586-0… @zhizhid @FeixiongCheng @ASaykin @PTenigma @chrisdav66 @lishenlc @birtutamtuz @wait_sasha @AdamMBrickman @njahanshad @KeckMedicineUSC @USC @USCLONI
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AI4AD Consortium retweeted
Paul Thompson
@PTenigma11 Nov 2023
#AI in Nature magazine: this week's issue features our AI/Machine Learning Consortium, creating AI methods for Alzheimer's disease nature.com/articles/d41586-0… Thanks @maxdkozlov for a great article + 1000000 congrats to @zhizhid @FeixiongCheng @ASaykin @chrisdav66 @lishenlc @birtutamtuz @wait_sasha @AdamMBrickman @njahanshad @AI4AD @KeckMedicineUSC @USC
A graphic of the brain is shown, with green and red colors highlight areas of abnormality using the random field theory, and the excursion sets of the Hotelling's T-squared distributed random field. Extensions of these methods using AI read in the entire scan to convolutional neural nets, variational autoencoders, or transformers to make inference about a person's diagnosis, prognosis, optimal treatment, and disease stage of progression

ALT A graphic of the brain is shown, with green and red colors highlight areas of abnormality using the random field theory, and the excursion sets of the Hotelling's T-squared distributed random field. Extensions of these methods using AI read in the entire scan to convolutional neural nets, variational autoencoders, or transformers to make inference about a person's diagnosis, prognosis, optimal treatment, and disease stage of progression

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AI4AD Consortium retweeted
Duygu Tosun@birtutamtuz12 Nov 2023
Replying to @AI4AD
@AI4AD featured in Nature magazine @UCSFimaging @UCSF_BCHSI
Paul Thompson
@PTenigma11 Nov 2023
#AI in Nature magazine: this week's issue features our AI/Machine Learning Consortium, creating AI methods for Alzheimer's disease nature.com/articles/d41586-0… Thanks @maxdkozlov for a great article + 1000000 congrats to @zhizhid @FeixiongCheng @ASaykin @chrisdav66 @lishenlc @birtutamtuz @wait_sasha @AdamMBrickman @njahanshad @AI4AD @KeckMedicineUSC @USC
A graphic of the brain is shown, with green and red colors highlight areas of abnormality using the random field theory, and the excursion sets of the Hotelling's T-squared distributed random field. Extensions of these methods using AI read in the entire scan to convolutional neural nets, variational autoencoders, or transformers to make inference about a person's diagnosis, prognosis, optimal treatment, and disease stage of progression

ALT A graphic of the brain is shown, with green and red colors highlight areas of abnormality using the random field theory, and the excursion sets of the Hotelling's T-squared distributed random field. Extensions of these methods using AI read in the entire scan to convolutional neural nets, variational autoencoders, or transformers to make inference about a person's diagnosis, prognosis, optimal treatment, and disease stage of progression

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Happy to share our #SfN2023 poster - '3D CNN Models for Brain Age Prediction from Diffusion MRI in North American and Indian Cohorts: Testing Generalizability.' We tested the generalizability of 3D CNNs trained on 636 controls dMRI from ADNI.
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AI4AD Consortium retweeted
Lab of Neuro Imaging
@USCLONI9 Oct 2023
We had a great time walking together to #ENDALZ 💜 in Hermosa Beach this past weekend ✌️🌷 Thanks to our faculty, staff, and students who participated, and thanks to @alzassociation for bringing so many people together 🤝🧠
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AI4AD Consortium retweeted
Paul Thompson
@PTenigma30 Sep 2023
AI in Medicine: How Do #AI methods Diagnose Disease? A Tutorial + Guided Tour (new VIDEO, recorded this afternoon!)
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AI4AD Consortium retweeted
Li Shen@lishenlc2 Oct 2023
Be sure to check out our newly established Biomedical Informatics track in the GCB PhD program.
UPenn GCB@UPennGCB2 Oct 2023
Applications for the 2024 academic year are officially open - and this year, there is NO application fee! Not only that, but we are excited to announce a new biomedical informatics-focused track of the GCB PhD program. Apply here by December 1st: applyweb.com/upenng/index.ft…
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AI4AD Consortium retweeted
Li Shen@lishenlc8 Oct 2023
Looking forward to Jacob Leiby (@UPennGCB), Zixuan Wen (amcs.upenn.edu/academic-prog…) and Boning Tong (@pennbioeng )'s presentations at sites.google.com/view/mlmi20…, and mmmi2023.github.io/. @UPennDBEI @UPennIBI @dokyoon_kim
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AI4AD Consortium retweeted
Duygu Tosun@birtutamtuz24 Aug 2023
An Unsupervised Data‐Driven Approach to Profiling Distinct Tau Progression Patterns - Tosun - 2022 - Alzheimer's & Dementia - Wiley Online Library alz-journals.onlinelibrary.w… @UCSFimaging @UCSF_Ci2 @AI4AD @SiemensHealth

An Unsupervised Data‐Driven Approach to Profiling Distinct Tau Progression Patterns

Background Given the central role that accumulation of tau pathology plays in the clinical progression of Alzheimer’s disease, the ability to quantify and predict the spread of tau is of great...

alz-journals.onlinelibrary.wiley.com
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AI4AD Consortium retweeted
Duygu Tosun@birtutamtuz29 Aug 2023
Profiling and predicting distinct tau progression patterns: An unsupervised data‐driven approach to flortaucipir positron emission tomography - Tosun - Alzheimer's & Dementia - Wiley Online Library alz-journals.onlinelibrary.w… @UCSFimaging @UCSF_Ci2 @AI4AD @SiemensHealth

Profiling and predicting distinct tau progression patterns: An unsupervised data‐driven approach to...

INTRODUCTION How to detect patterns of greater tau burden and accumulation is still an open question. METHODS An unsupervised data-driven whole-brain pattern analysis of longitudinal tau positron e...

alz-journals.onlinelibrary.wiley.com
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AI4AD Consortium retweeted
Duygu Tosun@birtutamtuz5 Sep 2023
Identifying individuals with non‐Alzheimer's disease co‐pathologies: A precision medicine approach to clinical trials in sporadic AD - Alzheimer's & Dementia - Wiley Online Library alz-journals.onlinelibrary.w… @UCSFimaging @UCSF_Ci2 @AI4AD @UCSF_BCHSI @NCIREveterans @TakedaPharma

Identifying individuals with non‐Alzheimer's disease co‐pathologies: A precision medicine approach...

INTRODUCTION Biomarkers remain mostly unavailable for non-Alzheimer's disease neuropathological changes (non-ADNC) such as transactive response DNA-binding protein 43 (TDP-43) proteinopathy, Lewy...

alz-journals.onlinelibrary.wiley.com
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AI4AD Consortium retweeted
Paul Thompson
@PTenigma12 Oct 2023
Invertible neural networks can help to synthesize realistic medical imaging data to augment training datasets for AI. This is similar to Cycle consistency in a generative adversarial network (GAN). Packed audience for Lena Maier Hein Keynote @DaliWorkshop @MICCAI_Society
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Join us for our DALI workshop this afternoon! 🖌️ We have THREE distinguished keynote speakers: Prof. Dimitris N. Metaxas, Prof. Lena Maier-Hein @lena_maierhein, and Prof. Paul M. Thompson @PTenigma and a mixed session of orals and posters! Program: dali-miccai.github.io/
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AI4AD Consortium retweeted
Paul Thompson
@PTenigma12 Oct 2023
Arrived in beautiful Vancouver ! If you are still here at #MICCAI2023, come see us at 130pm in room 14 for our #AI workshop (thanks to Yuan Xue for the invite!)
ADSMI Workshop @ MICCAI2024@DaliWorkshop12 Oct 2023
Join us for our DALI workshop this afternoon! 🖌️ We have THREE distinguished keynote speakers: Prof. Dimitris N. Metaxas, Prof. Lena Maier-Hein @lena_maierhein, and Prof. Paul M. Thompson @PTenigma and a mixed session of orals and posters! Program: dali-miccai.github.io/
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AI4AD Consortium retweeted
Paul Thompson
@PTenigma25 Jul 2023
祝贺中国青年学者主办了一场鼓舞人心的神经影像人工智能研讨会并享用了丰盛的晚宴！ (Congratulations to the Chinese Young Scholars for hosting an inspiring Neuroimaging #AI Symposium and a great dinner!) #OHBM2023 @ycgyan @lishenlc @anqi_qiu @RuiyangGe @YuPingWang12
A nice picture of scientists from the Chinese Young Scholars Society having dinner in Montréal after an excellent symposium on AI in Neuroimaging

ALT A nice picture of scientists from the Chinese Young Scholars Society having dinner in Montréal after an excellent symposium on AI in Neuroimaging

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Happening now, room 512 #OHBM2023 @OHBM @PTenigma
Paul Thompson
@PTenigma24 Jul 2023
JOIN US @515pm TODAY for "AI in Neuroimaging" - come + explore the latest breakthroughs in #AI technologies for understanding the human brain- @OHBM Room 512A-D, meet superstar speakers @jiangtz1 @ycgyan @anqi_qiu @sattertt @RuiyangGe more: rfmri.org/OHBM2023CN #OHBM2023
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@ASaykin, PhD, will chair and present in the basic science session, "What can we learn from multiomics?"
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@ApostolovaLiana, MD, presented the results from the Eli-Lilly Phase III clinical trial on Alzheimer's medication Donanemab.
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