{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T03:12:48Z","timestamp":1758078768540,"version":"3.44.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686196","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T00:00:00Z","timestamp":1757980800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9,16]]},"abstract":"<jats:p>Accurate lung nodule segmentation in computed tomography (CT) scans is a critical step in early lung cancer detection. While deep learning approaches have shown considerable success, challenges persist due to the small size, varied shape, and blurred boundaries of pulmonary nodules. To address these difficulties, we propose a learnable Partial Differential Equation (PDE) module that performs anisotropic diffusion as a trainable feature refinement layer within neural architectures. This module is fully differentiable and jointly optimized with the network, enabling adaptive spatial smoothing based on local gradient structures. By enhancing intra-region consistency and preserving important anatomical edges, the PDE module improves the quality of feature representation prior to prediction. Integrated into an enhanced PCAM-ResUNet \u2014 a modified variant of ResUNet++ with spatial and channel attention \u2014 the proposed approach achieves a Dice Similarity Coefficient of 90.22% on the LUNA16 dataset and 89.59% on its noise-augmented variant (LUNA-Noise), along with a Miss Rate of 9.69%, highlighting its strong detection sensitivity and robustness.<\/jats:p>","DOI":"10.3233\/faia250553","type":"book-chapter","created":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T13:20:02Z","timestamp":1758028802000},"source":"Crossref","is-referenced-by-count":0,"title":["Improving Lung Nodule Segmentation in CT Scans with a Learnable PDE-Based Diffusion Block"],"prefix":"10.3233","author":[{"given":"Khai Dinh","family":"Lai","sequence":"first","affiliation":[{"name":"University of Science, Ho Chi Minh City, Vietnam"},{"name":"Vietnam National University, Ho Chi Minh City, Vietnam"},{"name":"Saigon University, Ho Chi Minh City, Vietnam"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6801-1924","authenticated-orcid":false,"given":"Thai Hoang","family":"Le","sequence":"additional","affiliation":[{"name":"University of Science, Ho Chi Minh City, Vietnam"},{"name":"Vietnam National University, Ho Chi Minh City, Vietnam"}]},{"given":"Thanh Thuy","family":"Nguyen","sequence":"additional","affiliation":[{"name":"VNU University of Technology, Ha Noi City, Vietnam"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","New Trends in Intelligent Software Methodologies, Tools and Techniques"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA250553","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T13:20:03Z","timestamp":1758028803000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA250553"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,16]]},"ISBN":["9781643686196"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia250553","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,16]]}}}