5 papers
A Causal Framework for Mitigating Data Shifts in Healthcare
Kurt Butler, Stephanie Riley, Damian Machlanski +13
Developing predictive models that perform reliably across diverse patient populations and heterogeneous environments is a core aim of medical research. However, generalization is o…
CSEval: A Framework for Evaluating Clinical Semantics in Text-to-Image Generation
Robert Cronshaw, Konstantinos Vilouras, Junyu Yan +4
Text-to-image generation has been increasingly applied in medical domains for various purposes such as data augmentation and education. Evaluating the quality and clinical reliabil…
SWiFT: Soft-Mask Weight Fine-tuning for Bias Mitigation
Junyu Yan, Feng Chen, Yuyang Xue +4
Recent studies have shown that Machine Learning (ML) models can exhibit bias in real-world scenarios, posing significant challenges in ethically sensitive domains such as healthcar…
Anatomy-Grounded Weakly Supervised Prompt Tuning for Chest X-ray Latent Diffusion Models
Konstantinos Vilouras, Ilias Stogiannidis, Junyu Yan +2
Latent Diffusion Models have shown remarkable results in text-guided image synthesis in recent years. In the domain of natural (RGB) images, recent works have shown that such model…
Zero-Shot Medical Phrase Grounding with Off-the-shelf Diffusion Models
Konstantinos Vilouras, Pedro Sanchez, Alison Q. O'Neil +1
Localizing the exact pathological regions in a given medical scan is an important imaging problem that traditionally requires a large amount of bounding box ground truth annotation…