Kostas Kamnitsas
@kostaskamnitsas.bsky.social
Assoc Prof - ML for Medical Imaging - Univ of Oxford, Dept Engineering
Co-Director CDT Healthcare Data Science
Honor. Res. Fellow Imperial College & Univ of Birmingham
Creator http://deepmedic.org
Co-Director CDT Healthcare Data Science
Honor. Res. Fellow Imperial College & Univ of Birmingham
Creator http://deepmedic.org
🥳🎊🥂 She won it! She won it! She won it! 🎉🎊🥂
🏆🏅Best Paper Award #MICCAI2025 for our #MedIA article!🏅🏆
One of most prestigious awards in #Medical #Imaging that I could only dream when starting a lab 3 years ago, but @ziyunliang.bsky.social made it!
📜 Paper: www.sciencedirect.com/science/arti...
🧵👇
🏆🏅Best Paper Award #MICCAI2025 for our #MedIA article!🏅🏆
One of most prestigious awards in #Medical #Imaging that I could only dream when starting a lab 3 years ago, but @ziyunliang.bsky.social made it!
📜 Paper: www.sciencedirect.com/science/arti...
🧵👇
September 26, 2025 at 1:21 PM
🥳🎊🥂 She won it! She won it! She won it! 🎉🎊🥂
🏆🏅Best Paper Award #MICCAI2025 for our #MedIA article!🏅🏆
One of most prestigious awards in #Medical #Imaging that I could only dream when starting a lab 3 years ago, but @ziyunliang.bsky.social made it!
📜 Paper: www.sciencedirect.com/science/arti...
🧵👇
🏆🏅Best Paper Award #MICCAI2025 for our #MedIA article!🏅🏆
One of most prestigious awards in #Medical #Imaging that I could only dream when starting a lab 3 years ago, but @ziyunliang.bsky.social made it!
📜 Paper: www.sciencedirect.com/science/arti...
🧵👇
Really proud of the lab's progress and the growth of our students: Decentralised Isolation Networks (DIsoN) for out-of-distribution detection accepted to #NeurIPS2025 !
Whoop #NeurIPS2025 accepted! 🎉
Meet DIsoN, our 🧹💨 privacy-preserving OOD detector that compares test samples to training data without ever sharing the training data.
We make Out-of-Distribution detection decentralized!
📄Paper: arxiv.org/pdf/2506.09024
🧵👇
Meet DIsoN, our 🧹💨 privacy-preserving OOD detector that compares test samples to training data without ever sharing the training data.
We make Out-of-Distribution detection decentralized!
📄Paper: arxiv.org/pdf/2506.09024
🧵👇
September 20, 2025 at 9:08 AM
Really proud of the lab's progress and the growth of our students: Decentralised Isolation Networks (DIsoN) for out-of-distribution detection accepted to #NeurIPS2025 !
📣 New article on MedIA, a fantastic job by Ziyun:
🤖🏥IterMask3D: Unsupervised Anomaly Detection and Segmentation in 3D Brain MRI
-AI that detects problematic image artifacts (eg Quality Control for reliability)
-Advances unsupervised lesion segmentation
-No labels for training
Open Access & code👇
🤖🏥IterMask3D: Unsupervised Anomaly Detection and Segmentation in 3D Brain MRI
-AI that detects problematic image artifacts (eg Quality Control for reliability)
-Advances unsupervised lesion segmentation
-No labels for training
Open Access & code👇
September 19, 2025 at 10:45 AM
📣 New article on MedIA, a fantastic job by Ziyun:
🤖🏥IterMask3D: Unsupervised Anomaly Detection and Segmentation in 3D Brain MRI
-AI that detects problematic image artifacts (eg Quality Control for reliability)
-Advances unsupervised lesion segmentation
-No labels for training
Open Access & code👇
🤖🏥IterMask3D: Unsupervised Anomaly Detection and Segmentation in 3D Brain MRI
-AI that detects problematic image artifacts (eg Quality Control for reliability)
-Advances unsupervised lesion segmentation
-No labels for training
Open Access & code👇
Towards foundational models for Brain Lesion Segmentation with Multi-Modal MRI, vol.2 from our lab:
We show it s feasible to train 1 model with Federated Learning on clients with different brain lesions and MRI modalities, with performance similar to centralised.
Oral @wacvconference.bsky.social
We show it s feasible to train 1 model with Federated Learning on clients with different brain lesions and MRI modalities, with performance similar to centralised.
Oral @wacvconference.bsky.social
🚀Excited to share our latest work: 🧠FedUniBrain Framework, a necessary step towards training foundation models for multimodal MRIs with Federated Learning, accepted at #WACV25 and selected for an oral!
🔗 arXiv: arxiv.org/pdf/2406.11636
💻 GitHub: github.com/FelixWag/Fed...
🧵1/N
🔗 arXiv: arxiv.org/pdf/2406.11636
💻 GitHub: github.com/FelixWag/Fed...
🧵1/N
January 27, 2025 at 7:58 PM
Towards foundational models for Brain Lesion Segmentation with Multi-Modal MRI, vol.2 from our lab:
We show it s feasible to train 1 model with Federated Learning on clients with different brain lesions and MRI modalities, with performance similar to centralised.
Oral @wacvconference.bsky.social
We show it s feasible to train 1 model with Federated Learning on clients with different brain lesions and MRI modalities, with performance similar to centralised.
Oral @wacvconference.bsky.social