{"@context":"https://schema.org","@type":"CreativeWork","@id":"https://froggit.ai/public/capsules/283c75ce-2e2d-4120-984d-74ce29f5a083","identifier":"283c75ce-2e2d-4120-984d-74ce29f5a083","url":"https://froggit.ai/public/capsules/283c75ce-2e2d-4120-984d-74ce29f5a083","name":"Unsupervised Denoising of Real Clinical Low Dose Liver CT with Perceptual Attention Networks","text":"# Unsupervised Denoising of Real Clinical Low Dose Liver CT with Perceptual Attention Networks\n\nSource-backed public reference for arXiv:2605.00793.\n\n**Authors:** Zhilin Guan, Wei Zhang\n**Primary source:** https://arxiv.org/abs/2605.00793\n**Published:** 2026-05-01T17:19:15Z\n**Updated:** 2026-05-16T15:37:58Z\n**Categories:** eess.IV, cs.AI, cs.CV\n\n## Abstract Summary\nWith the development of deep learning, medical image processing has been widely used to assist clinical research. This paper focuses on the denoising problem of low-dose computed tomography using deep learning. Although low-dose computed tomography reduces radiation exposure to patients, it also introduces more noise, which may interfere with visual interpretation by physicians and affect diagnostic results. To address this problem, inspired by Cycle-GAN for unsupervised learning, this paper proposes an end-to-end unsupervised low-dose computed tomography denoising framework. The proposed framework combines a U-Net structure for multi-scale feature extraction, an attention mechanism for feature fusion, and a residual network for feature transformation. It also introduces perceptual loss to improve the network for the characteristics of medical images. In addition, we construct a real low-dose computed tomography dataset and design a large number of comparative experiments to validate the proposed method, using both image-based evaluation metrics and medical evaluation criteria. Compared with classical methods, the main advantage of this paper is that it addresses the limitation that real clinical data cannot be directly used for supervised learning, while still achieving...\n\n## Public Use Notes\n- This capsule summarizes the paper's arXiv metadata and abstract; it is not an independent replication or endorsement of the paper's claims.\n- Use it as a cited research reference for discovery, retrieval, and agent context.\n- For clinical, security, operational, or deployment-sensitive topics, treat the paper as ","keywords":["eess.IV","cs.AI","cs.CV"],"about":[],"citation":["https://arxiv.org/abs/2605.00793"],"isPartOf":{"@type":"Dataset","name":"Forge Cascade Knowledge Graph","url":"https://froggit.ai"},"publisher":{"@type":"Organization","name":"Forge Cascade","url":"https://froggit.ai"},"dateCreated":"2026-05-04T06:00:06.448000Z","dateModified":"2026-06-19T14:20:13Z","isBasedOn":"https://arxiv.org/abs/2605.00793","additionalProperty":[{"@type":"PropertyValue","name":"trust_level","value":40},{"@type":"PropertyValue","name":"verification_status","value":"sources_verified"},{"@type":"PropertyValue","name":"provenance_status","value":"valid"},{"@type":"PropertyValue","name":"evidence_level","value":"primary_source"}]}