🎓Interested in pursuing fully-funded PhD or PostDoc research in (medical) image computing or explainable machine learning in general? 🚨We are currently hiring in our xAI-Lab Universität Bamberg Get in touch and we can chat at #MICCAI2023 MICCAI Society Details below.

Come and see Shuting’s poster on predicting outcome after stroke at #MICCAI2023

Another great poster from BioMedIA Imperial by Margarita Bintsi at #MICCAI2023

On page 2 of RSIP Vision today #MICCAI2023 BioMedIA Imperial student Qiang Ma rsipvision.com/MICCAI2023-Wed… with Daniel Rueckert and Bernhard Kainz

Good morning MICCAI! MICCAI Daily of today (Wednesday) Important community information at page 23! Great poster reviews, great interviews and 5 mind-blowing slides of keynote Yann LeCun. ==> rsipvision.com/MICCAI2023-Wed… Happy reading and have a great day at MICCAI 2023! RSIP Vision

CoTAN for rapid, high-accuracy neonatal brain cortical surface reconstruction using MRI. With conditional attention mechanisms, Qiang Ma efficiently predicts surface deformations, achieving minimal errors. 🧠👶 Department AIBE (FAU Erlangen-Nürnberg) BioMedIA Imperial Borderless research group #MICCAI2023 W-05-023

#ML in medical image segmentation struggles with topological accuracy. We introduce the Euler Characteristic (EC) for efficient and accurate topological constraints, enhancing segmentation quality. 🧠🖼️ Department AIBE (FAU Erlangen-Nürnberg) BioMedIA Imperial Borderless research group #MICCAI2023 W-05-090

Interested in topology preserving brain segmentation? Check out Liu Li’s poster at #MICCAI2023

Conditional Temporal Attention Networks for cortical surface reconstruction by Qiang Ma at #MICCAI2023

Image synthesis could revolutionize clinical practice. Hadrien Reynaud developed a method to generate video sequences from single images in echocardiograms. 🚀🏥 Better by 38 points! Department AIBE (FAU Erlangen-Nürnberg) BioMedIA Imperial Borderless research group #MICCAI2023 arxiv.org/abs/2303.12644 poster W-06-037

Addressing motion in abdominal MRI, especially in IBD patients, we propose a deep adversarial super-resolution approach that corrects motion artefacts without needing detailed intestine knowledge or paired data. 🏥🖼️ Department AIBE (FAU Erlangen-Nürnberg) BioMedIA Imperial Borderless research group #MICCAI2023 W-06-062

Bye #MICCAI2023

Self-supervised single-class strategies with histogram-equalised gradient upscaling are showing promise. Johanna P Mueller ‘s approach surpasses existing benchmarks. 🧠🔍 #MICCAI23 UNSURE arxiv.org/abs/2303.13227 Department AIBE (FAU Erlangen-Nürnberg) BioMedIA Imperial Borderless research group #MICCAI2023

Very successful #MICCAI MOOD challenge medicalood.dkfz.de/web/ with our Team SubmitSomething winning all categories 🎉🎉 Department AIBE (FAU Erlangen-Nürnberg) BioMedIA Imperial Borderless research group #MICCAI2023 x.com/bernhardkainz1… x.com/bernhardkainz1…

📢📢📢 Job alert: new PhD position #MachineLearning for automated in-distribution testing together with industry hosted in Bavaria. de.indeed.com/viewjob?jk=3cf…

Fantastic work about interpretability constraints in large multi-modal language-vision #MachineLearning models by Mischa Dombrowski presented AAAI this week (oral).

Very nice idea to illustrate what we are doing Borderless research group during a talk. Thanks a lot to Jens Kleesiek and the excellent etim.uk-essen.de event for this!

💨 We explored Geoffrey Hinton's forward-forward ⏩⏩ learning concept and discovered some new methods to deliver rapid and resource-efficient adaptable #MachineLearning for numerous tasks in medical image analysis. More to come... ⏩⏩

Fully synthetic datasets are becoming a reality. We show how to make them in Hadrien Reynaud et al.'s latest #MICCAI24 work. Such datasets promise to solve significant data-sharing challenges in medicine by ensuring complete patient privacy, as it contains no data from real patients

🔎 New method in #MICCAI24 for distilling image data, analysing latent graphs, reducing the need for extensive datasets in histopathology. This allows for the creation of a small set of synthetic images that encapsulate the most critical information from large datasets.