Published in arXiv, 2024
This paper explores the use of synthetic data for robust stroke segmentation.
Recommended citation: Chalcroft, L., Pappas, I., Price, C.J., & Ashburner, J. (2024). Synthetic Data for Robust Stroke Segmentation. arXiv preprint. arXiv:2404.01946v1. http://arxiv.org/abs/2404.01946v1
Published in arXiv, 2024
This paper presents a robust ensemble algorithm for ischemic stroke lesion segmentation, demonstrating generalizability and clinical utility beyond the ISLES Challenge.
Recommended citation: de la Rosa, E., et al. (including Chalcroft, L.) (2024). A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge. arXiv preprint. arXiv:2403.19425. https://arxiv.org/abs/2403.19425
Published in 37th Conference on Neural Information Processing Systems (NeurIPS), 2023
This paper introduces LKA for efficient and robust brain lesion segmentation.
Recommended citation: Chalcroft, L.F., et al. (2023). LKA: Large-kernel Attention for Efficient and Robust Brain Lesion Segmentation. In 37th Conference on Neural Information Processing Systems (NeurIPS). https://arxiv.org/pdf/2308.07251
Published in ASMUS/MICCAI, 2021
This paper focuses on intraoperative ultrasound segmentation techniques.
Recommended citation: Chalcroft, L.F., et al. (2021). Development and evaluation of intraoperative ultrasound segmentation with negative image frames and multiple observer labels. In ASMUS/MICCAI. https://arxiv.org/pdf/2108.04114