1 citations · 2 across the 14 of their papers we have counts for
15 papers
Latent-to-Latent Flow for Volumetric Stochastic Segmentation
Omar Todd, Sooha Kim, Raghav Mehta +5
Uncertainty arising from inter-observer variability in medical image segmentation plays an important role in developing treatment plans. Research in this area is inhibited by the l…
Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
Giang Nguyen, Raghav Mehta, Emma A. M. Stanley +4
Foundation models are increasingly used as image feature extractors for mammography, but their robustness under external domain shift remains unclear. We benchmark 15 foundation-mo…
GRC-ProbNet: Uncertainty-aware Feature Extraction for Cardiovascular Disease Classification
Yash Shah, Omar Todd, Philipp Seeböck +3
The automatic detection and classification of cardiovascular disease (CVD) from computed tomography (CT) images plays an important role in clinical practice. Recently, a hybrid pip…
Counterfactual Stress Testing for Image Classification Models
Moritz Stammel, Fabio De Sousa Ribeiro, Raghav Mehta +2
Deep learning models in medical imaging often fail when deployed in new clinical environments due to distribution shifts in demographics, scanner hardware, or acquisition protocols…
Positional Segmentor-Guided Counterfactual Fine-Tuning for Spatially Localized Image Synthesis
Tian Xia, Matthew Sinclair, Andreas Schuh +8
Counterfactual image generation enables controlled data augmentation, bias mitigation, and disease modeling. However, existing methods guided by external classifiers or regressors…
Pixel-level Counterfactual Contrastive Learning for Medical Image Segmentation
Marceau Lafargue-Hauret, Raghav Mehta, Fabio De Sousa Ribeiro +2
Image segmentation relies on large annotated datasets, which are expensive and slow to produce. Silver-standard (AI-generated) labels are easier to obtain, but they risk introducin…
Segmentor-Guided Counterfactual Fine-Tuning for Locally Coherent and Targeted Image Synthesis
Tian Xia, Matthew Sinclair, Andreas Schuh +8
Counterfactual image generation is a powerful tool for augmenting training data, de-biasing datasets, and modeling disease. Current approaches rely on external classifiers or regre…
Flow Stochastic Segmentation Networks
Fabio De Sousa Ribeiro, Omar Todd, Charles Jones +3
We introduce the Flow Stochastic Segmentation Network (Flow-SSN), a generative segmentation model family featuring discrete-time autoregressive and modern continuous-time flow vari…
Exploring the interplay of label bias with subgroup size and separability: A case study in mammographic density classification
Emma A. M. Stanley, Raghav Mehta, Mélanie Roschewitz +2
Systematic mislabelling affecting specific subgroups (i.e., label bias) in medical imaging datasets represents an understudied issue concerning the fairness of medical AI systems.…
UNSURF: Uncertainty Quantification for Cortical Surface Reconstruction of Clinical Brain MRIs
Raghav Mehta, Karthik Gopinath, Ben Glocker +1
We propose UNSURF, a novel uncertainty measure for cortical surface reconstruction of clinical brain MRI scans of any orientation, resolution, and contrast. It relies on the discre…
Where are we with calibration under dataset shift in image classification?
Mélanie Roschewitz, Raghav Mehta, Fabio de Sousa Ribeiro +1
We conduct an extensive study on the state of calibration under real-world dataset shift for image classification. Our work provides important insights on the choice of post-hoc an…
CF-Seg: Counterfactuals meet Segmentation
Raghav Mehta, Fabio De Sousa Ribeiro, Tian Xia +4
Segmenting anatomical structures in medical images plays an important role in the quantitative assessment of various diseases. However, accurate segmentation becomes significantly…
Cardiovascular disease classification using radiomics and geometric features from cardiac CT
Ajay Mittal, Raghav Mehta, Omar Todd +3
Automatic detection and classification of Cardiovascular disease (CVD) from Computed Tomography (CT) images play an important part in facilitating better-informed clinical decision…
Factored Classifier-Free Guidance
Tian Xia, Fabio De Sousa Ribeiro, Rajat R Rasal +3
Counterfactual generation aims to simulate realistic hypothetical outcomes under causal interventions. Diffusion models have emerged as a powerful tool for this task, combining DDI…
Automatic dataset shift identification to support safe deployment of medical imaging AI
Mélanie Roschewitz, Raghav Mehta, Charles Jones +1
Shifts in data distribution can substantially harm the performance of clinical AI models and lead to misdiagnosis. Hence, various methods have been developed to detect the presence…