News

2 Papers accepted at MICCAI 2022

Our Papers titled “CASHformer: Cognition Aware SHape Transformer for Longitudinal Analysis” and “Is a PET all you need? A multi-modal study for Alzheimer’s disease using 3D CNNs ” have been accepted for publication at MICCAI 2022

 


Journal article accepted at Scientific Reports

Our Paper titled “Hippocampal Representations for Deep Learning on Alzheimer’s Disease” has been accepted for publication at Scientific Reports

 

Lecture series on AI & Healthcare

We started co-hosting the TUM Distinguished Lecture Series on AI & Healthcare with exciting talks from pioneers from the field.


2 Papers accepted at MICCAI 2021

Our Papers titled “Scalable, Axiomatic Explanations of Deep Alzheimer’s Diagnosis from Heterogeneous Data ” and “Combining 3D Image and Tabular Data via the Dynamic Affine Feature Map Transform ” have been accepted for publication at MICCAI 2021


3 Papers accepted at MLMI 2021

Our Papers titled “STRUDEL: Self-Training with Uncertainty Dependent Label Refinement across Domains“, “Alzheimer’s Disease Diagnosis via Deep Factorization Machine Models” and “TransforMesh: A Transformer Network for Longitudinal modeling of Anatomical Meshes” have been accepted for publication at MLMI 2021


Paper accepted at ISBI 2021

Our Paper titled “Geometric Deep Learning on Anatomical Meshes for the Prediction of Alzheimer’s Disease” has been accepted for publication at ISBI 2021


Journal article accepted at Medical Image Analysis

Our Paper titled “Detect and correct bias in multi-site neuroimaging datasets” has been accepted for publication at Medical Image Analysis.


Paper accepted at 3DV

Our Paper titled “Recalibration of Neural Networks for Point Cloud Analysis” has been accepted for publication at 3DV.


Journal article accepted at Medical Image Analysis

Our Paper titled “Discriminative and generative models for anatomical shape analysis on point clouds with deep neural networks” has been accepted for publication at Medical Image Analysis.


Paper accepted at ECML PKDD 2020

Our Paper titled Adversarial Learned Molecular Graph Inference and Generation has been accepted to be presented at The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. Our code is available at https://github.com/ai-med/almgig


Journal article accepted at IEEE Transactions on Medical Imaging

Our Paper titled “Recalibrating 3D ConvNets with Project & Excite ‘” has been accepted for publication at  IEEE Transactions on Medical Imaging.


DeepMentia started!

Happy to share that we have recently started DeepMentia. An inter-disciplinary project funded by BMBF for advancing deep learning in dementia. Joint project between LMU and TUM, with hands-on clinical expertise from Igor Yakushev. For more information, see the DeepMentia Website.


Sebastian Pölsterl won the Miccai Educational Challenge 2020

We congratulate Sebastian Pölsterl for winning the Miccai Educational Challenge 2020 with his Tutorial on Survival Analysis for Deep Learning.


Radio interview on Bayern 2 on “AI in Medicine”

Sendung auf BR2 über “Künstliche Intelligenz in der Medizin” mit einem Interview von Christian Wachinger.


Journal article accepted at Medical Image Analysis

Our Paper titled ” ‘Squeeze & Excite’ Guided Few-Shot Segmentation of Volumetric Images ” has been accepted for publication at Medical Image Analysis.


Two papers accepted at MICCAI 2019

AI-med will present two papers at the annual conference of Medical Imaging Computing and Computer Assisted interventions (MICCAI)to be held in Shenzen with the following papers:

    • Quantifying Confounding Bias in Neuroimaging Datasets with Causal Inference
    • ‘Project & Excite’ Modules for Segmentation of Volumetric Medical Scans

 


Journal article accepted at NeuroImage

Our Paper titled “Bayesian QuickNAT: Model uncertainty in deep whole-brain segmentation for structure-wise quality control” has been accepted for publication at NeuroImage.

 


Data vs. Dementia

Interview about our work on large-scale data analysis in dementia with AI.
English version
German version


Article accepted at IPMI 2019

Our paper Learning a Conditional Generative Model for Anatomical Shape Analysis has been accepted to be presented at “Information Processing in Medical Imaging” (IPMI) 2019. The conference will take place in Hong Kong between the 2th and 7th of June.

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Interview: Künstliche Intelligenz

Interview in the current issue of Münchner Uni Magazin on Artificial Intelligence. Importance of artificial intelligence for the analysis of medical images.

 


Webservice online

Check out our Webservice for brain segmentation.

 


Journal article accepted at NeuroImage

Our Paper titled “QuickNAT: A Fully Convolutional Network for Quick and Accurate Segmentation of Neuroanatomy” has been accepted for publication at NeuroImage.

 


Journal article accepted at IEEE TMI

Our paper Recalibrating Fully Convolutional Networks with Spatial and Channel ‘Squeeze & Excitation’ Blocks, has been accepted at IEEE Transactions on Medical Imaging.


Paper accepted at ShapeMI 2018

Our paper Deep Shape Analysis on Abdominal Organs for Diabetes Prediction  has been accepted for presentation at the ShapeMI workshop, a sattelite event at MICCAI 2018.


Journal article accepted at IEEE TMI

Our work on Keypoint Transfer for Fast Whole-Body Segmentation has been accepted at IEEE Transactions on Medical Imaging. Check out the Video Animation of the Algorithm!


Three papers accepted at MICCAI 2018!

AI-med will be present at the annual conference of Medical Imaging Computing and Computer Assisted interventions (MICCAI) to be held in Granada with the following papers:


ShapeMI: Shape in Medical Imaging 2018

We co-organize the workshop ShapeMI: Shape in Medical Imaging, which is held in conjunction with MICCAI 2018. Submit your work!


New article in Biological Psychiatry!

Our work on A Longitudinal Imaging Genetics Study of Neuroanatomical Asymmetry in Alzheimer’s Disease has been accepted for publication in Biological Psychiatry.


New article in Neuroimage!

Our most recent work on using uncertainty in age estimation to measure abnormal brain development has been accepted for publication in Neuroimage! The preprint version is already available in arXiv.


AI-Med at SAP ML Retreat

A Brief Insight into SAP’s first ML Research Retreat 
Our team members Benjamin Gutierrrez and Abhijit Guha Roy presented our current projects on SAP’s first Machine Learning Research Retreat.


News article about our work on AI and age estimation

How AI Will Push the Frontiers of Modern Medicine
Interview with Sophia Haegerich on our Brain Age Anomaly project with SAP and the future of AI in healthcare.


Nomination for Young Scientist Award at MICCAI 2017

We are happy to announce that Abhijit Guha Roy was one of 12 students nominated for the Young Scientist Award at MICCAI 2017.


Student Travel Awards at MICCAI 2017

Our MICCAI 2017 papers “A Multi-Armed Bandit to Smartly Select a Training Set from Big Medical Data” by Benjamin Gutierrez and “Error Corrective Boosting for Learning Fully Convolutional Networks with Limited Data” by Abhijit Guha Roy obtained a Student Travel Award.


3 papers accepted at MICCAI 2017 MICCAI 2017

Check out our Publications Page


Junior research group funding from ZD.B

Christian Wachinger received funding for creating a junior research group on “Computational Population Modeling from Big Medical Image Data” from Zentrum Digitalisierung Bayern.

https://www.uni-muenchen.de/forschung/news/2017/wachinger_forschergruppe.html

https://www.km.bayern.de/pressemitteilung/10537/nr-034-vom-10-02-2017.html


DeepNAT published in NeuroImage

DeepNAT: Deep Convolutional Neural Network for Segmenting Neuroanatomy


Article on asymmetry in Alzheimer’s Disease published in Brain

Using BrainPrint to compute brain asymmetry, we found that asymmetry increases with the progression of dementia. Whole-brain Analysis Reveals Increased Neuroanatomical Asymmetries in Dementia for Hippocampus and Amygdala (PDF).

The asymmetry article was featured as Research highlight in the journal Nature Reviews Neurology

http://www.uni-muenchen.de/forschung/news/2016/wachinger_alzheimer.html

http://www.en.uni-muenchen.de/news/newsarchiv/2016/wachinger_alzheimer.html