Search Results for author:
Found 26 papers, 10 papers with code
The Hardness of Validating Observational Studies with Experimental Data
Using the framework of impossible inference, we show that although it is possible to use experimental data to \emph{falsify} causal effect estimates from observational data, in general it is not possible to \emph{validate} such estimates.
Contrastive representations of high-dimensional, structured treatments
We address this challenge by devising a novel contrastive approach to learn a representation of the high-dimensional treatments, and prove that it identifies underlying causal factors and discards non-causally relevant factors.
Benchmarking Counterfactual Image Generation
Our framework is implemented in a user-friendly Python package that can be extended to incorporate additional SCMs, causal methods, generative models, and datasets for the community to build on.
Non-parametric identifiability and sensitivity analysis of synthetic control models
While identifiability of the causal estimand in such models has been obtained from a range of assumptions, it is widely and implicitly assumed that the underlying assumptions are satisfied for all time periods both pre- and post-intervention.
Self-Supervised 3D Human Pose Estimation in Static Video Via Neural Rendering
Inferring 3D human pose from 2D images is a challenging and long-standing problem in the field of computer vision with many applications including motion capture, virtual reality, surveillance or gait analysis for sports and medicine.
Adnexal Mass Segmentation with Ultrasound Data Synthesis
Ovarian cancer is the most lethal gynaecological malignancy.
nnOOD: A Framework for Benchmarking Self-supervised Anomaly Localisation Methods
It is also difficult to assess whether a task generalises well for universal anomaly detection, as they are often only tested on a limited range of anomalies.
A Review of Causality for Learning Algorithms in Medical Image Analysis
Medical image analysis is a vibrant research area that offers doctors and medical practitioners invaluable insight and the ability to accurately diagnose and monitor disease.
Is More Data All You Need? A Causal Exploration
Curating a large scale medical imaging dataset for machine learning applications is both time consuming and expensive.
D'ARTAGNAN: Counterfactual Video Generation
Causally-enabled machine learning frameworks could help clinicians to identify the best course of treatments by answering counterfactual questions.
Estimating Categorical Counterfactuals via Deep Twin Networks
To learn causal mechanisms satisfying these constraints, and perform counterfactual inference with them, we introduce deep twin networks.
Detecting Hypo-plastic Left Heart Syndrome in Fetal Ultrasound via Disease-specific Atlas Maps
Fetal ultrasound screening during pregnancy plays a vital role in the early detection of fetal malformations which have potential long-term health impacts.
Ultrasound Video Transformers for Cardiac Ejection Fraction Estimation
We achieve an average frame distance of 3. 36 frames for the ES and 7. 17 frames for the ED on videos of arbitrary length.
Video Summarization through Reinforcement Learning with a 3D Spatio-Temporal U-Net
Intelligent video summarization algorithms allow to quickly convey the most relevant information in videos through the identification of the most essential and explanatory content while removing redundant video frames.
Next-Gen Machine Learning Supported Diagnostic Systems for Spacecraft
Future short or long-term space missions require a new generation of monitoring and diagnostic systems due to communication impasses as well as limitations in specialized crew and equipment.
Unsupervised Human Pose Estimation through Transforming Shape Templates
Human pose estimation is a major computer vision problem with applications ranging from augmented reality and video capture to surveillance and movement tracking.
Topological Information Retrieval with Dilation-Invariant Bottleneck Comparative Measures
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 embedding the graphical structure of the database into a manifold in a hierarchy-preserving manner using a variety of metrics.
Causal Future Prediction in a Minkowski Space-Time
In this paper we propose a novel theoretical framework to perform causal future prediction by embedding spatiotemporal information on a Minkowski space-time.
3D Probabilistic Segmentation and Volumetry from 2D projection images
X-Ray imaging is quick, cheap and useful for front-line care assessment and intra-operative real-time imaging (e. g., C-Arm Fluoroscopy).
Ultrasound Video Summarization using Deep Reinforcement Learning
We show that our method is superior to alternative video summarization methods and that it preserves essential information required by clinical diagnostic standards.
Correlation of Auroral Dynamics and GNSS Scintillation with an Autoencoder
High energy particles originating from solar activity travel along the the Earth's magnetic field and interact with the atmosphere around the higher latitudes.
Prediction of GNSS Phase Scintillations: A Machine Learning Approach
We propose a novel architecture and loss function to predict 1 hour in advance the magnitude of phase scintillations within a time window of plus-minus 5 minutes with state-of-the-art performance.
Flexible Conditional Image Generation of Missing Data with Learned Mental Maps
Real-world settings often do not allow acquisition of high-resolution volumetric images for accurate morphological assessment and diagnostic.
Multiple Landmark Detection using Multi-Agent Reinforcement Learning
We compare our approach with state-of-the-art architectures and achieve significantly better accuracy by reducing the detection error by 50%, while requiring fewer computational resources and time to train compared to the naive approach of training K agents separately.
Deep Segmentation and Registration in X-Ray Angiography Video
In interventional radiology, short video sequences of vein structure in motion are captured in order to help medical personnel identify vascular issues or plan intervention.
The RNN-ELM Classifier
In this paper we examine learning methods combining the Random Neural Network, a biologically inspired neural network and the Extreme Learning Machine that achieve state of the art classification performance while requiring much shorter training time.
