It is well-known that the Tseng algorithm and its modifications have been successfully employed in approximating zeros of the sum of monotone operators. In this study, we restored various thoracic diseases' computerized tomography (CT) images, which were degraded with a known blur function and additive noise, using a modified Tseng algorithm. The test images used in the study depict calcification of the Aorta, Subcutaneous Emphysema, Tortuous Aorta, Pneumomediastinum, and Pneumoperitoneum. Additionally, we employed well-known image restoration tools to enhance image quality and compared the quality of restored images with the originals. Finally, the study demonstrates the potential to advance monotone inclusion problem-solving, particularly in the field of medical image recovery.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11268622 | PMC |
| http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0305728 | PLOS |
Publication Analysis
Top Keywords
Similar Publications
Prediction of Persistent Tumor Status in Nasopharyngeal Carcinoma Post-Radiotherapy-Related Treatment: A Machine Learning Approach.
Cancers (Basel)
December 2024
Artificial Intelligence Center, Chung Shan Medical University Hospital, Taichung 40201, Taiwan.
The duration of the response to radiotherapy-related treatment is a critical prognostic indicator for patients with nasopharyngeal carcinoma (NPC). Persistent tumor status, including residual tumor presence and early recurrence, is associated with poorer survival outcomes. To address this, we developed a prediction model to identify patients at a high risk of persistent tumor status prior to initiating treatment.
View Article and Find Full Text PDFA machine learning approach for identification of vascular access patency in hemodialysis patients using photoplethysmography: A pilot study.
J Vasc Access
December 2024
Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Yunlin, Taiwan.
Introduction: Vascular access (VA) is essential for patients with hemodialysis, and its dysfunction is a major complication that can reduce quality of life or even threaten life. VA patency is not only difficult to predict on an individual basis, but also challenging to predict in real-time. To overcome this challenge, this study aimed to develop a machine learning approach to predict 6-month primary patency (PP) using photoplethysmography (PPG) signals acquired from the tips of both index fingers.
View Article and Find Full Text PDFIdentifying the presence of atrial fibrillation during sinus rhythm using a dual-input mixed neural network with ECG coloring technology.
BMC Med Res Methodol
December 2024
Institute of Statistics, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
Background: Undetected atrial fibrillation (AF) poses a significant risk of stroke and cardiovascular mortality. However, diagnosing AF in real-time can be challenging as the arrhythmia is often not captured instantly. To address this issue, a deep-learning model was developed to diagnose AF even during periods of arrhythmia-free windows.
View Article and Find Full Text PDFComprehensive evaluation and practical guideline of gating methods for high-dimensional cytometry data: manual gating, unsupervised clustering, and auto-gating.
Brief Bioinform
November 2024
Drug Discovery Institute, School of Medicine, University of Pittsburgh, 700 Technology Dr, Pittsburgh, PA 15219, US.
Cytometry is an advanced technique for simultaneously identifying and quantifying many cell surface and intracellular proteins at a single-cell resolution. Analyzing high-dimensional cytometry data involves identifying and quantifying cell populations based on their marker expressions. This study provided a quantitative review and comparison of various ways to phenotype cellular populations within the cytometry data, including manual gating, unsupervised clustering, and supervised auto-gating.
View Article and Find Full Text PDFUsing Large Language Models for Efficient Cancer Registry Coding in the Real Hospital Setting: A Feasibility Study.
Pac Symp Biocomput
December 2024
Department of Computer Science, National Yang Ming Chiao Tung University Hsinchu, 300093, Taiwan, ROC, Taiwan.
The primary challenge in reporting cancer cases lies in the labor-intensive and time-consuming process of manually reviewing numerous reports. Current methods predominantly rely on rule-based approaches or custom-supervised learning models, which predict diagnostic codes based on a single pathology report per patient. Although these methods show promising evaluation results, their biased outcomes in controlled settings may hinder adaption to real-world reporting workflows.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!