10 citations · 31 across the 12 of their papers we have counts for
14 papers
A Critical Perspective on Finite Sample Conformal Prediction Theory in Medical Applications
Klaus-Rudolf Kladny, Bernhard Schölkopf, Lisa Koch +2
Machine learning (ML) is transforming healthcare, but safe clinical decisions demand reliable uncertainty estimates that standard ML models fail to provide. Conformal prediction (C…
Subgroup Performance Analysis in Hidden Stratifications
Alceu Bissoto, Trung-Dung Hoang, Tim Flühmann +3
Machine learning (ML) models may suffer from significant performance disparities between patient groups. Identifying such disparities by monitoring performance at a granular level…
Conformal Performance Range Prediction for Segmentation Output Quality Control
Anna M. Wundram, Paul Fischer, Michael Muehlebach +2
Recent works have introduced methods to estimate segmentation performance without ground truth, relying solely on neural network softmax outputs. These techniques hold potential fo…
Attri-Net: A Globally and Locally Inherently Interpretable Model for Multi-Label Classification Using Class-Specific Counterfactuals
Susu Sun, Stefano Woerner, Andreas Maier +2
Interpretability is crucial for machine learning algorithms in high-stakes medical applications. However, high-performing neural networks typically cannot explain their predictions…
Right for the Wrong Reason: Can Interpretable ML Techniques Detect Spurious Correlations?
Susu Sun, Lisa M. Koch, Christian F. Baumgartner
While deep neural network models offer unmatched classification performance, they are prone to learning spurious correlations in the data. Such dependencies on confounding informat…
Deep Hypothesis Tests Detect Clinically Relevant Subgroup Shifts in Medical Images
Lisa M. Koch, Christian M. Schürch, Christian F. Baumgartner +2
Distribution shifts remain a fundamental problem for the safe application of machine learning systems. If undetected, they may impact the real-world performance of such systems or…
Inherently Interpretable Multi-Label Classification Using Class-Specific Counterfactuals
Susu Sun, Stefano Woerner, Andreas Maier +2
Interpretability is essential for machine learning algorithms in high-stakes application fields such as medical image analysis. However, high-performing black-box neural networks d…
Uncertainty Quantification in CNN-Based Surface Prediction Using Shape Priors
Katarína Tóthová, Sarah Parisot, Matthew C. H. Lee +5
Surface reconstruction is a vital tool in a wide range of areas of medical image analysis and clinical research. Despite the fact that many methods have proposed solutions to the r…
Learning to Segment Medical Images with Scribble-Supervision Alone
Yigit B. Can, Krishna Chaitanya, Basil Mustafa +3
Semantic segmentation of medical images is a crucial step for the quantification of healthy anatomy and diseases alike. The majority of the current state-of-the-art segmentation al…
Visual Feature Attribution using Wasserstein GANs
Christian F. Baumgartner, Lisa M. Koch, Kerem Can Tezcan +2
Attributing the pixels of an input image to a certain category is an important and well-studied problem in computer vision, with applications ranging from weakly supervised localis…
An Exploration of 2D and 3D Deep Learning Techniques for Cardiac MR Image Segmentation
Christian F. Baumgartner, Lisa M. Koch, Marc Pollefeys +1
Accurate segmentation of the heart is an important step towards evaluating cardiac function. In this paper, we present a fully automated framework for segmentation of the left (LV)…
Employing Weak Annotations for Medical Image Analysis Problems
Martin Rajchl, Lisa M. Koch, Christian Ledig +4
To efficiently establish training databases for machine learning methods, collaborative and crowdsourcing platforms have been investigated to collectively tackle the annotation eff…
SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound
Christian F. Baumgartner, Konstantinos Kamnitsas, Jacqueline Matthew +5
Identifying and interpreting fetal standard scan planes during 2D ultrasound mid-pregnancy examinations are highly complex tasks which require years of training. Apart from guiding…
Multi-Atlas Segmentation using Partially Annotated Data: Methods and Annotation Strategies
Lisa M. Koch, Martin Rajchl, Wenjia Bai +5
Multi-atlas segmentation is a widely used tool in medical image analysis, providing robust and accurate results by learning from annotated atlas datasets. However, the availability…