4.6k citations · 6.9k across the 75 of their papers we have counts for
84 papers
Shadow Reduction in Ultrasound Imaging Using Differentiable Simulation and Radiance Field Decomposition
Valentin Bacher, Pak Hei Yeung, Bernhard Kainz +4
Acoustic shadows from bone and other highly attenuating tissues obscure clinically important structures in ultrasound. In fetal brain imaging, skull-induced artefacts disproportion…
Sculpting Efficiency: Pruning Medical Imaging Models for On-Device Inference
Sudarshan Sreeram, Bernhard Kainz
Leveraging ML advancements to augment healthcare systems can improve patient outcomes. Yet, uninformed engineering decisions in early-stage research inadvertently hinder the feasib…
LesionMix: A Lesion-Level Data Augmentation Method for Medical Image Segmentation
Berke Doga Basaran, Weitong Zhang, Mengyun Qiao +3
Data augmentation has become a de facto component of deep learning-based medical image segmentation methods. Most data augmentation techniques used in medical imaging focus on spat…
Conditional Temporal Attention Networks for Neonatal Cortical Surface Reconstruction
Qiang Ma, Liu Li, Vanessa Kyriakopoulou +4
Cortical surface reconstruction plays a fundamental role in modeling the rapid brain development during the perinatal period. In this work, we propose Conditional Temporal Attentio…
Many tasks make light work: Learning to localise medical anomalies from multiple synthetic tasks
Matthew Baugh, Jeremy Tan, Johanna P. Müller +3
There is a growing interest in single-class modelling and out-of-distribution detection as fully supervised machine learning models cannot reliably identify classes not included in…
Zero-Shot Anomaly Detection with Pre-trained Segmentation Models
Matthew Baugh, James Batten, Johanna P. Müller +1
This technical report outlines our submission to the zero-shot track of the Visual Anomaly and Novelty Detection (VAND) 2023 Challenge. Building on the performance of the WINCLIP f…
Quantifying Sample Anonymity in Score-Based Generative Models with Adversarial Fingerprinting
Mischa Dombrowski, Bernhard Kainz
Recent advances in score-based generative models have led to a huge spike in the development of downstream applications using generative models ranging from data augmentation over…
Realistic Data Enrichment for Robust Image Segmentation in Histopathology
Sarah Cechnicka, James Ball, Hadrien Reynaud +3
Poor performance of quantitative analysis in histopathological Whole Slide Images (WSI) has been a significant obstacle in clinical practice. Annotating large-scale WSIs manually i…
Trade-offs in Fine-tuned Diffusion Models Between Accuracy and Interpretability
Mischa Dombrowski, Hadrien Reynaud, Johanna P. Müller +2
Recent advancements in diffusion models have significantly impacted the trajectory of generative machine learning research, with many adopting the strategy of fine-tuning pre-train…
Confidence-Aware and Self-Supervised Image Anomaly Localisation
Johanna P. Müller, Matthew Baugh, Jeremy Tan +2
Universal anomaly detection still remains a challenging problem in machine learning and medical image analysis. It is possible to learn an expected distribution from a single class…
Feature-Conditioned Cascaded Video Diffusion Models for Precise Echocardiogram Synthesis
Hadrien Reynaud, Mengyun Qiao, Mischa Dombrowski +5
Image synthesis is expected to provide value for the translation of machine learning methods into clinical practice. Fundamental problems like model robustness, domain transfer, ca…
Foreground-Background Separation through Concept Distillation from Generative Image Foundation Models
Mischa Dombrowski, Hadrien Reynaud, Matthew Baugh +1
Curating datasets for object segmentation is a difficult task. With the advent of large-scale pre-trained generative models, conditional image generation has been given a significa…
Self-Supervised 3D Human Pose Estimation in Static Video Via Neural Rendering
Luca Schmidtke, Benjamin Hou, Athanasios Vlontzos +1
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,…
ParaDime: A Framework for Parametric Dimensionality Reduction
Andreas Hinterreiter, Christina Humer, Bernhard Kainz +1
ParaDime is a framework for parametric dimensionality reduction (DR). In parametric DR, neural networks are trained to embed high-dimensional data items in a low-dimensional space…
Adnexal Mass Segmentation with Ultrasound Data Synthesis
Clara Lebbos, Jen Barcroft, Jeremy Tan +5
Ovarian cancer is the most lethal gynaecological malignancy. The disease is most commonly asymptomatic at its early stages and its diagnosis relies on expert evaluation of transvag…
Improved post-hoc probability calibration for out-of-domain MRI segmentation
Cheng Ouyang, Shuo Wang, Chen Chen +4
Probability calibration for deep models is highly desirable in safety-critical applications such as medical imaging. It makes output probabilities of deep networks interpretable, b…
nnOOD: A Framework for Benchmarking Self-supervised Anomaly Localisation Methods
Matthew Baugh, Jeremy Tan, Athanasios Vlontzos +2
The wide variety of in-distribution and out-of-distribution data in medical imaging makes universal anomaly detection a challenging task. Recently a number of self-supervised metho…
D'ARTAGNAN: Counterfactual Video Generation
Hadrien Reynaud, Athanasios Vlontzos, Mischa Dombrowski +4
Causally-enabled machine learning frameworks could help clinicians to identify the best course of treatments by answering counterfactual questions. We explore this path for the cas…
Placenta Segmentation in Ultrasound Imaging: Addressing Sources of Uncertainty and Limited Field-of-View
Veronika A. Zimmer, Alberto Gomez, Emily Skelton +10
Automatic segmentation of the placenta in fetal ultrasound (US) is challenging due to the (i) high diversity of placenta appearance, (ii) the restricted quality in US resulting in…
A Review of Causality for Learning Algorithms in Medical Image Analysis
Athanasios Vlontzos, Daniel Rueckert, Bernhard Kainz
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. Machi…
CAS-Net: Conditional Atlas Generation and Brain Segmentation for Fetal MRI
Liu Li, Qiang Ma, Matthew Sinclair +6
Fetal Magnetic Resonance Imaging (MRI) is used in prenatal diagnosis and to assess early brain development. Accurate segmentation of the different brain tissues is a vital step in…
CortexODE: Learning Cortical Surface Reconstruction by Neural ODEs
Qiang Ma, Liu Li, Emma C. Robinson +3
We present CortexODE, a deep learning framework for cortical surface reconstruction. CortexODE leverages neural ordinary differential equations (ODEs) to deform an input surface in…
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…