{"type":"ScholarlyArticle","title":"Spatially Covariant Lesion Segmentation","authors":[{"name":"Hang Zhang","slug":"hang-zhang","given":"Hang","family":"Zhang","affiliation":"Cornell University"},{"name":"Rongguang Wang","slug":"rongguang-wang","given":"Rongguang","family":"Wang","affiliation":"University of Pennsylvania"},{"name":"Jinwei Zhang","slug":"jinwei-zhang","given":"Jinwei","family":"Zhang","affiliation":"Cornell University"},{"name":"Dongdong Liu","slug":"dongdong-liu","given":"Dongdong","family":"Liu","affiliation":"New York University"},{"name":"Chao Li","slug":"chao-li","given":"Chao","family":"Li","affiliation":"Cornell University"},{"name":"Jiahao Li","slug":"jiahao-li","given":"Jiahao","family":"Li","affiliation":"Cornell University"}],"abstract":"Compared to natural images, medical images usually show stronger visual patterns and therefore this adds flexibility and elasticity to resource-limited clinical applications by injecting proper priors into neural networks.\r\nIn this paper, we propose spatially covariant pixel-aligned classifier (SCP) to improve the computational efficiency and meantime maintain or increase accuracy for lesion segmentation.\r\nSCP relaxes the spatial invariance constraint imposed by convolutional operations and optimizes an underlying implicit function that maps image coordinates to network weights, the parameters of which are obtained along with the backbone network training and later used for generating network weights to capture spatially covariant contextual information. \r\nWe demonstrate the effectiveness and efficiency of the proposed SCP using two lesion segmentation tasks from different imaging modalities: white matter hyperintensity segmentation in magnetic resonance imaging and liver tumor segmentation in contrast-enhanced abdominal computerized tomography.\r\nThe network using SCP has achieved 23.8, 64.9 and 74.7 reduction in GPU memory usage, FLOPs, and network size with similar or better accuracy for lesion segmentation.","doi":"10.24963/ijcai.2023/190","url":"https://ijcai.org/proceedings/2023/190","pdf":"https://ijcai.org/proceedings/2023/0190.pdf","markdown":"https://ijcai.org/proceedings/2023/190.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/190","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Computer Vision","track":"Main Track","number":190,"pages":{"first":1713,"last":1721},"keywords":["Computer Vision: CV: Biomedical image analysis","Machine Learning: ML: Convolutional networks","Computer Vision: CV: Segmentation"],"videos":[],"references":[{"position":1,"raw":"[Abraham et al., 2014] Alexandre Abraham, Fabian Pe- dregosa, Michael Eickenberg, Philippe Gervais, Andreas Mueller, Jean Kossaifi, Alexandre Gramfort, Bertrand Thirion, and Gaël Varoquaux. 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