{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/xai-resunet-analysing-the-impact-of-pre","title":"XAI-ResUNet: Analysing the Impact of Pre-training in ResUNet Architectures for Multiple Sclerosis Lesion Segmentation using EigenGradCAM","arxiv_id":null,"date":"2024-09-25","proceeding":"IMVIP 2024 9","authors":["Vayangi Ganepola","Prateek Mathur","Oluwabukola Adegboro","Julia Dietlmeier","Aonghus Lawlor","Noel E. O’Connor","Claudia Mazo"],"abstract":"Multiple Sclerosis (MS) is a chronic disease that causes lesions in the central nervous system. Diagnosing MS is a challenging process that strongly relies on Magnetic Resonance Imaging (MRI) and exhibits high and largely unexplained variability across patients. In this paper, we propose a novel XAI-ResUNet architecture by integrating ResNet-50 as an encoder in a U-Net-like architecture and demystifying the detection and segmentation process by incorporating EigenGradCAM into the encoder. We used the training and testing sets of the MSSEG-2016 dataset for our experiments. We compared three architectures with ResNet-50 pre-trained on ImageNet, RadImageNet, and without pre-training. Our preliminary results show that ResNet-50 pre-trained on ImageNet outperforms the others, achieving a DSC of 0.638. We also demonstrate how pre-training on large image datasets can affect MS lesion detection and segmentation. Our code is publicly available at https://github.com/VishmiVishara/XAI-ResUNet.","url_abs":"https://digital-library.theiet.org/doi/pdf/10.1049/icp.2024.3318","url_pdf":"https://digital-library.theiet.org/doi/pdf/10.1049/icp.2024.3318","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"xai-resunet-analysing-the-impact-of-pre","repo_url":"https://github.com/VishmiVishara/XAI-ResUNet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"xai","task_name":"Explainable Artificial Intelligence (XAI)"},{"task_slug":"lesion-detection","task_name":"Lesion Detection"},{"task_slug":"lesion-segmentation","task_name":"Lesion Segmentation"},{"task_slug":"medical-image-segmentation","task_name":"Medical Image Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}