News
New paper published at Nature Communications
We are excited to share the new paper by Ertunc Erdil et al. "Predicting standardized uptake value of brown adipose tissue from CT scans using convolutional neural networks.
BMIC goes to MICCAI 2024
We are happy to announce that the paper "Vision-Based Neurosurgical Guidance: Unsupervised Localization and Camera-Pose Prediction" by Gary Sarwin et al. got accepted early for #MICCAI2024!
Online Tool for Anatomic Detection in Pituitary Surgery
We’re happy to announce the development of a website where researchers and clinicians can upload their videos of pituitary surgery and obtain detections of various anatomical structures and surgical instruments.
New paper accepted at Scientific Reports
We are happy to share that the paper titled "Predicting mortality after transcatheter aortic valve replacement using preprocedural CT" from David Brüggemann et al. has been accepted to Scientific Reports!
Two papers from BMIC group accepted at the upcoming NeurIPS 2023 conference
We are excited to present two new papers accepted for NeurIPS 2023
New paper accepted at IPMI 2023
We are happy to share the paper "Live Image-Based Neurosurgical Guidance and Roadmap Generation Using Unsupervised Embedding” by newly admitted PhD student Gary Sarwin.
BMIC goes to ICLR 2023 in Rwanda!
Congratulations to Tianfei Zhou and Gustav Bredell for their accepted papers.
Tianfei Zhou and his colleagues has won the Elsevier-MedAI Prize at MICCAI 2022
Congratulations for this prestigious prize!
Katarina Tothova got the best paper award in the UNSURE workshop at MICCAI 2022
Congratulations to Katarina Tothova and colleagues for winning the UNSURE2022 best paper award with their work on quantification of predictive uncertainty via inference time sampling!
It is with great pleasure that we announce that MIDL 2022 will be hosted in Zürich by ETHZ and UZH
We are looking forward to welcoming everyone to the charming city as well as stimulating a fruitful exchange of research. MIDL 2022 is organised by Ender Konukoglu (ETHZ) and Bjoern Menze (UZH).
Quasi-Dense Similarity Learning for Multiple Object Tracking
Similarity learning has been recognized as a crucial step for object tracking. However, existing multiple object tracking methods only use sparse ground truth matching as the training objective, while ignoring the majority of the informative regions on the images.
Instance-Aware Predictive Navigation in Multi-Agent Environments
In this work, we aim to achieve efficient end-to-end learning of driving policies in dynamic multi-agent environments. Predicting and anticipating future events at the object level are critical for making informed driving decisions.
Interview with Prof. Ender Konukoglu
Ender Konukoglu talks about his research at the intersection of medicine and engineering and about the challenges that arise when linking these two forward-looking fields.