output
20042026
most citedGravitational wave energy budget in strongly supercooled phase transitions

299 citations

Showing 2023Show all

7 papers · 1 filter

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.LG202329 cited

An Iterative Method for Unsupervised Robust Anomaly Detection Under Data Contamination

Minkyung Kim, Jongmin Yu, Junsik Kim +2

Most deep anomaly detection models are based on learning normality from datasets due to the difficulty of defining abnormality by its diverse and inconsistent nature. Therefore, it…

eess.IV202335 cited

Uncertainty Aware Training to Improve Deep Learning Model Calibration for Classification of Cardiac MR Images

Tareen Dawood, Chen Chen, Baldeep S. Sidhua +9

Quantifying uncertainty of predictions has been identified as one way to develop more trustworthy artificial intelligence (AI) models beyond conventional reporting of performance m…

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…

eess.IV202314 cited

Spatial gradient consistency for unsupervised learning of hyperspectral demosaicking: Application to surgical imaging

Peichao Li, Muhammad Asad, Conor Horgan +3

Hyperspectral imaging has the potential to improve intraoperative decision making if tissue characterisation is performed in real-time and with high-resolution. Hyperspectral snaps…

cs.LG2023

Unsupervised Deep One-Class Classification with Adaptive Threshold based on Training Dynamics

Minkyung Kim, Junsik Kim, Jongmin Yu +1

One-class classification has been a prevailing method in building deep anomaly detection models under the assumption that a dataset consisting of normal samples is available. In pr…