21 citations · 27 across the 8 of their papers we have counts for
8 papers
Tokenizer-Generator Coupling in Medical Image Generation
Liam Chalcroft
Latent medical image generators usually treat the tokenizer as fixed preprocessing. We test whether this separation holds in a controlled ChestMNIST study at that cros…
GAZE: Grounded Agentic Zero-shot Evaluation with Viewer-Level Tools and Literature Retrieval on Rare Brain MRI
Duaa Alim, Mogtaba Alim, Liam Chalcroft
Vision-language models (VLMs) read an image and produce text in a single forward pass, whereas radiologists typically inspect an image several times and consult the literature befo…
Unified 3D MRI Representations via Sequence-Invariant Contrastive Learning
Liam Chalcroft, Jenny Crinion, Cathy J. Price +1
Self-supervised deep learning has accelerated 2D natural image analysis but remains difficult to translate into 3D MRI, where data are scarce and pre-trained 2D backbones cannot ca…
Domain-Agnostic Stroke Lesion Segmentation Using Physics-Constrained Synthetic Data
Liam Chalcroft, Jenny Crinion, Cathy J. Price +1
Segmenting stroke lesions in MRI is challenging due to diverse acquisition protocols that limit model generalisability. In this work, we introduce two physics-constrained approache…
A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge
Ezequiel de la Rosa, Mauricio Reyes, Sook-Lei Liew +55
Diffusion-weighted MRI (DWI) is essential for stroke diagnosis, treatment decisions, and prognosis. However, image and disease variability hinder the development of generalizable A…
Synthetic Data for Robust Stroke Segmentation
Liam Chalcroft, Ioannis Pappas, Cathy J. Price +1
Current deep learning-based approaches to lesion segmentation in neuroimaging often depend on high-resolution images and extensive annotated data, limiting clinical applicability.…
Large-kernel Attention for Efficient and Robust Brain Lesion Segmentation
Liam Chalcroft, Ruben Lourenço Pereira, Mikael Brudfors +5
Vision transformers are effective deep learning models for vision tasks, including medical image segmentation. However, they lack efficiency and translational invariance, unlike co…
Development and evaluation of intraoperative ultrasound segmentation with negative image frames and multiple observer labels
Liam F Chalcroft, Jiongqi Qu, Sophie A Martin +8
When developing deep neural networks for segmenting intraoperative ultrasound images, several practical issues are encountered frequently, such as the presence of ultrasound frames…