4.6k citations · 6.9k across the 75 of their papers we have counts for
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Cross Modality 3D Navigation Using Reinforcement Learning and Neural Style Transfer
Cesare Magnetti, Hadrien Reynaud, Bernhard Kainz
This paper presents the use of Multi-Agent Reinforcement Learning (MARL) to perform navigation in 3D anatomical volumes from medical imaging. We utilize Neural Style Transfer to cr…
PRETUS: A plug-in based platform for real-time ultrasound imaging research
Alberto Gomez, Veronika A. Zimmer, Gavin Wheeler +8
We present PRETUS -a Plugin-based Real Time UltraSound software platform for live ultrasound image analysis and operator support. The software is lightweight; functionality is brou…
PialNN: A Fast Deep Learning Framework for Cortical Pial Surface Reconstruction
Qiang Ma, Emma C. Robinson, Bernhard Kainz +2
Traditional cortical surface reconstruction is time consuming and limited by the resolution of brain Magnetic Resonance Imaging (MRI). In this work, we introduce Pial Neural Networ…
Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and Localization
Hannah M. Schlüter, Jeremy Tan, Benjamin Hou +1
We introduce a simple and intuitive self-supervision task, Natural Synthetic Anomalies (NSA), for training an end-to-end model for anomaly detection and localization using only nor…
Estimating Categorical Counterfactuals via Deep Twin Networks
Athanasios Vlontzos, Bernhard Kainz, Ciaran M. Gilligan-Lee
Counterfactual inference is a powerful tool, capable of solving challenging problems in high-profile sectors. To perform counterfactual inference, one requires knowledge of the und…
Contrastive Learning for View Classification of Echocardiograms
Agisilaos Chartsias, Shan Gao, Angela Mumith +4
Analysis of cardiac ultrasound images is commonly performed in routine clinical practice for quantification of cardiac function. Its increasing automation frequently employs deep l…
Can non-specialists provide high quality gold standard labels in challenging modalities?
Samuel Budd, Thomas Day, John Simpson +5
Probably yes. -- Supervised Deep Learning dominates performance scores for many computer vision tasks and defines the state-of-the-art. However, medical image analysis lags behind…
Detecting Hypo-plastic Left Heart Syndrome in Fetal Ultrasound via Disease-specific Atlas Maps
Samuel Budd, Matthew Sinclair, Thomas Day +11
Fetal ultrasound screening during pregnancy plays a vital role in the early detection of fetal malformations which have potential long-term health impacts. The level of skill requi…
Detecting Outliers with Poisson Image Interpolation
Jeremy Tan, Benjamin Hou, Thomas Day +3
Supervised learning of every possible pathology is unrealistic for many primary care applications like health screening. Image anomaly detection methods that learn normal appearanc…
Ultrasound Video Transformers for Cardiac Ejection Fraction Estimation
Hadrien Reynaud, Athanasios Vlontzos, Benjamin Hou +3
Cardiac ultrasound imaging is used to diagnose various heart diseases. Common analysis pipelines involve manual processing of the video frames by expert clinicians. This suffers fr…
RATCHET: Medical Transformer for Chest X-ray Diagnosis and Reporting
Benjamin Hou, Georgios Kaissis, Ronald Summers +1
Chest radiographs are one of the most common diagnostic modalities in clinical routine. It can be done cheaply, requires minimal equipment, and the image can be diagnosed by every…
Video Summarization through Reinforcement Learning with a 3D Spatio-Temporal U-Net
Tianrui Liu, Qingjie Meng, Jun-Jie Huang +3
Intelligent video summarization algorithms allow to quickly convey the most relevant information in videos through the identification of the most essential and explanatory content…
Unsupervised Human Pose Estimation through Transforming Shape Templates
Luca Schmidtke, Athanasios Vlontzos, Simon Ellershaw +3
Human pose estimation is a major computer vision problem with applications ranging from augmented reality and video capture to surveillance and movement tracking. In the medical co…
Topological Information Retrieval with Dilation-Invariant Bottleneck Comparative Measures
Yueqi Cao, Athanasios Vlontzos, Luca Schmidtke +2
Appropriately representing elements in a database so that queries may be accurately matched is a central task in information retrieval; recently, this has been achieved by embeddin…