{"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/lesion-focused-super-resolution","title":"Lesion Focused Super-Resolution","arxiv_id":"1810.06693","date":"2018-10-15","proceeding":null,"authors":["Jin Zhu","Guang Yang","Pietro Lio"],"abstract":"Super-resolution (SR) for image enhancement has great importance in medical\nimage applications. Broadly speaking, there are two types of SR, one requires\nmultiple low resolution (LR) images from different views of the same object to\nbe reconstructed to the high resolution (HR) output, and the other one relies\non the learning from a large amount of training datasets, i.e., LR-HR pairs. In\nreal clinical environment, acquiring images from multi-views is expensive and\nsometimes infeasible. In this paper, we present a novel Generative Adversarial\nNetworks (GAN) based learning framework to achieve SR from its LR version. By\nperforming simulation based studies on the Multimodal Brain Tumor Segmentation\nChallenge (BraTS) datasets, we demonstrate the efficacy of our method in\napplication of brain tumor MRI enhancement. Compared to bilinear interpolation\nand other state-of-the-art SR methods, our model is lesion focused, which is\nnot only resulted in better perceptual image quality without blurring, but also\nmore efficient and directly benefit for the following clinical tasks, e.g.,\nlesion detection and abnormality enhancement. Therefore, we can envisage the\napplication of our SR method to boost image spatial resolution while\nmaintaining crucial diagnostic information for further clinical tasks.","url_abs":"http://arxiv.org/abs/1810.06693v1","url_pdf":"http://arxiv.org/pdf/1810.06693v1.pdf","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":"lesion-focused-super-resolution","repo_url":"https://github.com/GinZhu/MIASSR","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"lesion-focused-super-resolution","repo_url":"https://github.com/GinZhu/RDST","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"brain-tumor-segmentation","task_name":"Brain Tumor Segmentation"},{"task_slug":"diagnostic","task_name":"Diagnostic"},{"task_slug":"image-enhancement","task_name":"Image Enhancement"},{"task_slug":"lesion-detection","task_name":"Lesion Detection"},{"task_slug":"super-resolution","task_name":"Super-Resolution"},{"task_slug":"tumor-segmentation","task_name":"Tumor Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}