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
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 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 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:
Overview of the field of Deep Learning and its application to healthcare, with a focus on medical imaging. Delivered as part of a series of guest lectures for the COMP0900: Intro to Deep Learning module for MSc students in the Department of Computer Science.
Graduate course, University College London, Department of Computer Science, 2021
An introductory course on deep learning, starting from the machine learning fundamentals to at the end of the class have an understanding of the theoretical and practical aspects of deep learning