activity
20222024
most citedUPL-SFDA: Uncertainty-aware Pseudo Label Guided Source-Free Domain Adaptation for Medical Image Segmentation

61 citations · 192 across the 13 of their papers we have counts for

collaborators

13 papers

eess.IV20244 cited

An unsupervised learning-based shear wave tracking method for ultrasound elastography

Remi Delaunay, Yipeng Hu, Tom Vercauteren

Shear wave elastography involves applying a non-invasive acoustic radiation force to the tissue and imaging the induced deformation to infer its mechanical properties. This work in…

eess.IV202320 cited

Long-term Dependency for 3D Reconstruction of Freehand Ultrasound Without External Tracker

Qi Li, Ziyi Shen, Qian Li +5

Objective: Reconstructing freehand ultrasound in 3D without any external tracker has been a long-standing challenge in ultrasound-assisted procedures. We aim to define new ways of…

cs.CV202361 cited

UPL-SFDA: Uncertainty-aware Pseudo Label Guided Source-Free Domain Adaptation for Medical Image Segmentation

Jianghao Wu, Guotai Wang, Ran Gu +6

Domain Adaptation (DA) is important for deep learning-based medical image segmentation models to deal with testing images from a new target domain. As the source-domain data are us…

cs.CV202317 cited

Unified Brain MR-Ultrasound Synthesis using Multi-Modal Hierarchical Representations

Reuben Dorent, Nazim Haouchine, Fryderyk Kögl +9

We introduce MHVAE, a deep hierarchical variational auto-encoder (VAE) that synthesizes missing images from various modalities. Extending multi-modal VAEs with a hierarchical laten…

cs.CV20231 cited

Privileged Anatomical and Protocol Discrimination in Trackerless 3D Ultrasound Reconstruction

Qi Li, Ziyi Shen, Qian Li +5

Three-dimensional (3D) freehand ultrasound (US) reconstruction without using any additional external tracking device has seen recent advances with deep neural networks (DNNs). In t…

eess.IV20231 cited

Deep Homography Prediction for Endoscopic Camera Motion Imitation Learning

Martin Huber, Sebastien Ourselin, Christos Bergeles +1

In this work, we investigate laparoscopic camera motion automation through imitation learning from retrospective videos of laparoscopic interventions. A novel method is introduced…