We present and analyze the behavior of an evolutionary algorithm designed to estimate the parameters of a complex organ behavior model. The model is adaptable to account for patient's specificities. The aim is to finely tune the model to be accurately adapted to various real patient datasets. It can then be embedded, for example, in high fidelity simulations of the human physiology. We present here an application focused on respiration modeling. The algorithm is automatic and adaptive. A compound fitness function has been designed to take into account for various quantities that have to be minimized. The algorithm efficiency is experimentally analyzed on several real test cases: 1) three patient datasets have been acquired with the "breath hold" protocol, and 2) two datasets corresponds to 4-D CT scans. Its performance is compared with two traditional methods (downhill simplex and conjugate gradient descent): a random search and a basic real-valued genetic algorithm. The results show that our evolutionary scheme provides more significantly stable and accurate results.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1109/TBME.2012.2213251 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
BMT: A Cross-Validated ThinPrep Pap Cervical Cytology Dataset for Machine Learning Model Training and Validation.
Sci Data
December 2024
Department of Pathology and Laboratory Medicine, Alpert Medical School, Brown University, Providence, RI, 02912, USA.
In the past several years, a few cervical Pap smear datasets have been published for use in clinical training. However, most publicly available datasets consist of pre-segmented single cell images, contain on-image annotations that must be manually edited out, or are prepared using the conventional Pap smear method. Multicellular liquid Pap image datasets are a more accurate reflection of current cervical screening techniques.
View Article and Find Full Text PDFERCPMP: an endoscopic image and video dataset for colorectal polyps morphology and pathology.
BMC Res Notes
December 2024
Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
This dataset contains demographic, morphological and pathological data, endoscopic images and videos of 191 patients with colorectal polyps. Morphological data is included based on the latest international gastroenterology classification references such as Paris, Pit and JNET classification. Pathological data includes the diagnosis of the polyps including Tubular, Villous, Tubulovillous, Hyperplastic, Serrated, Inflammatory and Adenocarcinoma with Dysplasia Grade & Differentiation.
View Article and Find Full Text PDFCausal relationship between Hashimoto's thyroiditis and non-ischemic cardiomyopathy and the mediating role of inflammatory cytokines: Evidence from genetic association analysis.
Int J Cardiol
December 2024
Department of Cardiology, the First Affiliated Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing 210029, China. Electronic address:
Background: Previous studies report that Hashimoto's thyroiditis (HT) may be associated with non-ischemic cardiomyopathy (NICM); However, the causal relationship remains to be elucidated. Here, we aimed to investigate the causal relationship between HT and NICM through Mendelian randomization (MR) and explore the potential mediating role of inflammatory cytokines within this association.
Methods: The bidirectional two-sample MR, multivariable MR and mediation MR analyses were conducted based on genome-wide association study summary datasets, and MR results were further supported by multiple sensitivity analysis methods.
Development and validation of machine learning models for predicting venous thromboembolism in colorectal cancer patients: A cohort study in China.
Int J Med Inform
December 2024
Chongqing Cancer Multiomics Big Data Application Engineering Research Center, Chongqing University Cancer Hospital, Chongqing 400030, China. Electronic address:
Background: With advancements in healthcare, traditional VTE risk assessment tools are increasingly insufficient to meet the demands of high-quality care, underscoring the need for innovative and specialized assessment methods.
Objective: Owing to the remarkable success of machine learning in supervised learning and disease prediction, our objective is to develop a reliable and efficient model for assessing VTE risk by leveraging the fundamental data and clinical characteristics of colorectal cancer patients within our medical facility.
Methods: Six commonly used machine learning algorithms were utilized in our study to predict the occurrence of VTE in patients with rectal cancer.
YY1 drives PARP1 expression essential for PARylation of NONO in mRNA maturation during neuroblastoma progression.
J Transl Med
December 2024
Department of Pediatric Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, 430022, Hubei Province, People's Republic of China.
Background: Neuroblastoma (NB), the most prevalent solid tumor in children, arises from sympathetic nervous system and accounts for 15% of pediatric cancer mortality. This malignancy exhibits substantial genetic and clinical heterogeneity, thus complicating treatment strategies. Poly(ADP-ribose) polymerase 1 (PARP1), a key enzyme catalyzing polyADP-ribosylation (PARylation), plays critical roles in various cellular processes, and contributes to tumorigenesis and aggressiveness.
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!