Hyperconnected Openings Codified in a Max Tree Structure: An Application for Skull-Stripping in Brain MRI T1

Paredes-Orta, Carlos A.; Mendiola-Santibáñez, Jorge D.; Ibrahimi, Danjela; Rodríguez‐Reséndiz, Juvenal; Díaz-Flórez, Germán; Olvera-Olvera, Carlos

doi:10.3390/s22041378

Cited by 6 publications

(2 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Medical imaging and signal processing play an increasingly crucial role as key data sources in the field of medicine. It contains key information such as the patient's physiology, pathology, and genetics (Paredes-Orta et al, 2022 ). Through these data, medical professionals can obtain patients' physiological information, pathological features, and genetic background, enabling more accurate diagnoses and personalized treatment plans.…”

Section: Introductionmentioning

confidence: 99%

Integration of natural and deep artificial cognitive models in medical images: BERT-based NER and relation extraction for electronic medical records

Guo,

Liu,

Niu

2023

Front. Neurosci.

View full text Add to dashboard Cite

IntroductionMedical images and signals are important data sources in the medical field, and they contain key information such as patients' physiology, pathology, and genetics. However, due to the complexity and diversity of medical images and signals, resulting in difficulties in medical knowledge acquisition and decision support.MethodsIn order to solve this problem, this paper proposes an end-to-end framework based on BERT for NER and RE tasks in electronic medical records. Our framework first integrates NER and RE tasks into a unified model, adopting an end-to-end processing manner, which removes the limitation and error propagation of multiple independent steps in traditional methods. Second, by pre-training and fine-tuning the BERT model on large-scale electronic medical record data, we enable the model to obtain rich semantic representation capabilities that adapt to the needs of medical fields and tasks. Finally, through multi-task learning, we enable the model to make full use of the correlation and complementarity between NER and RE tasks, and improve the generalization ability and effect of the model on different data sets.Results and discussionWe conduct experimental evaluation on four electronic medical record datasets, and the model significantly out performs other methods on different datasets in the NER task. In the RE task, the EMLB model also achieved advantages on different data sets, especially in the multi-task learning mode, its performance has been significantly improved, and the ETE and MTL modules performed well in terms of comprehensive precision and recall. Our research provides an innovative solution for medical image and signal data.

show abstract

Section: Introductionmentioning

confidence: 99%

Integration of natural and deep artificial cognitive models in medical images: BERT-based NER and relation extraction for electronic medical records

Guo,

Liu,

Niu

2023

Front. Neurosci.

View full text Add to dashboard Cite

show abstract

“…For example, as we age, white matter volume (WMV) grows and then decreases, gray matter volume (GMV) decreases, and cerebrospinal fluid (CSF) gradually rises [ 1 , 2 ]. Magnetic resonance imaging (MRI), a non-invasive technique, makes it possible to use structural and functional neuroimaging data to predict age [ 3 , 4 , 5 , 6 ]. These studies can contribute to our understanding of the relationship between brain morphometry and age variation.…”

Section: Introductionmentioning

confidence: 99%

Interpretation for Individual Brain Age Prediction Based on Gray Matter Volume

Sun

Meng

et al. 2022

Brain Sciences

View full text Add to dashboard Cite

The relationship between age and the central nervous system (CNS) in humans has been a classical issue that has aroused extensive attention. Especially for individuals, it is of far greater importance to clarify the mechanisms between CNS and age. The primary goal of existing methods is to use MR images to derive high-accuracy predictions for age or degenerative diseases. However, the associated mechanisms between the images and the age have rarely been investigated. In this paper, we address the correlation between gray matter volume (GMV) and age, both in terms of gray matter themselves and their interaction network, using interpretable machine learning models for individuals. Our goal is not only to predict age accurately but more importantly, to explore the relationship between GMV and age. In addition to targeting each individual, we also investigate the dynamic properties of gray matter and their interaction network with individual age. The results show that the mean absolute error (MAE) of age prediction is 7.95 years. More notably, specific locations of gray matter and their interactions play different roles in age, and these roles change dynamically with age. The proposed method is a data-driven approach, which provides a new way to study aging mechanisms and even to diagnose degenerative brain diseases.

show abstract

MI brain-computer interfaces: A concise overview

Mandal,

Naskar

2023

Biomedical Signal Processing and Control

View full text Add to dashboard Cite

Hyperconnected Openings Codified in a Max Tree Structure: An Application for Skull-Stripping in Brain MRI T1

Cited by 6 publications

References 35 publications

Integration of natural and deep artificial cognitive models in medical images: BERT-based NER and relation extraction for electronic medical records

Integration of natural and deep artificial cognitive models in medical images: BERT-based NER and relation extraction for electronic medical records

Interpretation for Individual Brain Age Prediction Based on Gray Matter Volume

MI brain-computer interfaces: A concise overview

Contact Info

Product

Resources

About