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20242026
most citedMetrics that matter: Evaluating image quality metrics for medical image generation

3 citations · 5 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2026

A Calibrated Memorization Index (MI) for Detecting Training Data Leakage in Generative MRI Models

Yash Deo, Yan Jia, Toni Lassila +5

Image generative models are known to duplicate images from the training data as part of their outputs, which can lead to privacy concerns when used for medical image generation. We…

cs.CL2025★ 1 cited

WER is Unaware: Assessing How ASR Errors Distort Clinical Understanding in Patient Facing Dialogue

Zachary Ellis, Jared Joselowitz, Yash Deo +7

As Automatic Speech Recognition (ASR) is increasingly deployed in clinical dialogue, standard evaluations still rely heavily on Word Error Rate (WER). This paper challenges that st…

eess.IV2025★ 3 cited

Metrics that matter: Evaluating image quality metrics for medical image generation

Yash Deo, Yan Jia, Toni Lassila +5

Evaluating generative models for synthetic medical imaging is crucial yet challenging, especially given the high standards of fidelity, anatomical accuracy, and safety required for…

cs.CV2024★ 1 cited

GS-EMA: Integrating Gradient Surgery Exponential Moving Average with Boundary-Aware Contrastive Learning for Enhanced Domain Generalization in Aneurysm Segmentation

Fengming Lin, Yan Xia, Michael MacRaild +6

The automated segmentation of cerebral aneurysms is pivotal for accurate diagnosis and treatment planning. Confronted with significant domain shifts and class imbalance in 3D Rotat…

cs.CV2024

Unsupervised Domain Adaptation for Brain Vessel Segmentation through Transwarp Contrastive Learning

Fengming Lin, Yan Xia, Michael MacRaild +6

Unsupervised domain adaptation (UDA) aims to align the labelled source distribution with the unlabelled target distribution to obtain domain-invariant predictive models. Since cros…