📢 MELBA is growing its editorial team!
We’re excited to welcome three new Associate Editors who bring outstanding expertise in machine learning and biomedical imaging.
Welcome to:
🔹 @bernhardkainz.bsky.social
🔹 @mariavak.bsky.social
🔹 Lisa Koch
We’re thrilled to have you on board! 🚀
Congratulations to Qiang Ma for passing his PhD viva yesterday evening! And thanks to Bruce Fischl and Abhijeet Ghosh for acting as examiners!
Congratulations to Liu Li for passing her PhD viva today! And thanks to Björn Menze and Björn Schuller for acting as examiners!
Great BioMedIA Christmas party at Imperial!
Thank you to my co-authors @bernhardkainz.bsky.social and C. Müller
Looking for a London PhD? The London interdisciplinary social science DTP takes student designed PhD projects in a very broad range of topics from health innovation to sustainability and public policy liss-dtp.ac.uk
yes, exactly. Ensembles of methods usually achieve the best performance for challenging problems like this.
I mean the hidden biases can be learned, not only the known parameters. One can check with a parametrised model if a new input conforms with all the biases in a specific downstream model. This does not necessary need to be ethnicity but could be a combination of latent factors.
finding a method that allows us to estimate how suitable a model is for a given patient... This solves only one side of the problem but bias is not necessarily 'bad'. After all, 'patient specific' means 100% bias towards one specific patient. Triage and patient stratification will be key in future.
The 4th International Workshop of Advances in Simplifying Medical UltraSound (ASMUS) - a workshop held in conjunction with #MICCAI23 is only a few days away. Join us on
Sunday 8️⃣ Oct
from 8️⃣ AM
in Meeting Room 8️⃣
Vancouver Convention Center East Building Level 1.
miccai-ultrasound.github.io#/asmus23
- Multiple Instance Learning for Axillary Lymph Node Metastasis (DEMI)
x.com/Hanna_Paulal...
- Lesion-Level Data Augmentation for Segmentation (DALI)
arxiv.org/abs/2308.09026
- Probabilistic Self-Supervised Image Anomaly Localisation (UNSURE)
x.com/Hanna_Paulal...
- Synthetic Data Enrichment for Robust Image Segmentation in Kidney Transplant Pathology (DART)
arxiv.org/abs/2304.09534
- Neonatal Cortical Surface Reconstruction
arxiv.org/abs/2307.11870
- Unsupervised anomaly localisation with synthetic tasks
arxiv.org/abs/2307.00899
- Respiratory Motion Correction and Super-Resolution for 3D Abdominal MRI
link.springer.com/chapter/10.1...
- Robust Segmentation via Topology Violation Detection
link.springer.com/chapter/10.1...
- Cascaded Diffusion Models to generate echocardiogram videos
x.com/reynaud_h/st...
Getting ready for #MICCAI23! group -> @CityofVancouver ✈️
📢 This year we present work on 🧵:
#HiSciSky! My team and I are immersed in the world of intelligent algorithms for #MedicalImaging. We are on a mission to redefine real-time diagnostics especially for early detection and health screening. We explore normative machine learning to help defining what ought to be physiologically normal.
Hello BlueSky. Yet another platform or better?
