Diagnosis of brain diseases in fusion of neuroimaging modalities using deep learning: A review

Shoeibi, Afshin; Khodatars, Marjane; Jafari, Mahboobeh; Ghassemi, Navid; Moridian, Parisa; Alizadesani, Roohallah; Ling, Sai Ho; Khosravi, Abbas; Alinejad‐Rokny, Hamid; Lam, Hak‐Keung; Fuller‐Tyszkiewicz, Matthew; Acharya, U. Rajendra; Anderson, Donovan; Zhang, Yudong; Górriz, Juan Manuel

doi:10.1016/j.inffus.2022.12.010

Cited by 43 publications

(13 citation statements)

References 219 publications

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“…As the name implies, RF is made of multiple decision trees each of which consists of multiple decision and leaf nodes. For a classification problem with C classes, the training dataset features are used to create the nodes of the decision trees such that the Gini impurity measure is minimized 28 : where is the probability that a sample from class i is picked in node n. After creating the RF, upon receiving a test sample, it is passed down to each decision tree level by level until it reaches a leaf node. The final step of RF is aggregation of the decision tree outputs.…”

Section: Methodsmentioning

confidence: 99%

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Prognosis prediction in traumatic brain injury patients using machine learning algorithms

Khalili

Rismani

Nematollahi

et al. 2023

Sci Rep

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Predicting treatment outcomes in traumatic brain injury (TBI) patients is challenging worldwide. The present study aimed to achieve the most accurate machine learning (ML) algorithms to predict the outcomes of TBI treatment by evaluating demographic features, laboratory data, imaging indices, and clinical features. We used data from 3347 patients admitted to a tertiary trauma centre in Iran from 2016 to 2021. After the exclusion of incomplete data, 1653 patients remained. We used ML algorithms such as random forest (RF) and decision tree (DT) with ten-fold cross-validation to develop the best prediction model. Our findings reveal that among different variables included in this study, the motor component of the Glasgow coma scale, the condition of pupils, and the condition of cisterns were the most reliable features for predicting in-hospital mortality, while the patients’ age takes the place of cisterns condition when considering the long-term survival of TBI patients. Also, we found that the RF algorithm is the best model to predict the short-term mortality of TBI patients. However, the generalized linear model (GLM) algorithm showed the best performance (with an accuracy rate of 82.03 ± 2.34) in predicting the long-term survival of patients. Our results showed that using appropriate markers and with further development, ML has the potential to predict TBI patients’ survival in the short- and long-term.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Prognosis prediction in traumatic brain injury patients using machine learning algorithms

Khalili

Rismani

Nematollahi

et al. 2023

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…Ref. [ 20 ] proposed neural network collaboration-based multiple extraction techniques with an F1 value of 76.35 for the i2b2/UTHealth 2010 dataset. On the i2b2/UTHealth 2010 dataset, [ 21 ] developed a model-based multi-sequence labeling method with an F1 value of 76.17.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Deep Learning Approach for Accurate Tumor Detection in Medical Imaging Data

2023

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The automated extraction of critical information from electronic medical records, such as oncological medical events, has become increasingly important with the widespread use of electronic health records. However, extracting tumor-related medical events can be challenging due to their unique characteristics. To address this difficulty, we propose a novel approach that utilizes Generative Adversarial Networks (GANs) for data augmentation and pseudo-data generation algorithms to improve the model’s transfer learning skills for various tumor-related medical events. Our approach involves a two-stage pre-processing and model training process, where the data is cleansed, normalized, and augmented using pseudo-data. We evaluate our approach using the i2b2/UTHealth 2010 dataset and observe promising results in extracting primary tumor site size, tumor size, and metastatic site information. The proposed method has significant implications for healthcare and medical research as it can extract vital information from electronic medical records for oncological medical events.

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“…Deep learning is the most popular technique for brain disease diagnosis. [86,87] By modeling the association between brain images and disease, it fits a variety of complex mapping relationships and reduces dependence on feature extraction engineering, such as gray statistical histograms [88] and graph modeling maps. [89] This approach will show more robust and superior performance as datasets are extended.…”

Section: Brain Disease Diagnosismentioning

confidence: 99%

Understanding the brain with attention: A survey of transformers in brain sciences

Chen,

Wang,

Chen

et al. 2023

Brain-X

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Owing to their superior capabilities and advanced achievements, Transformers have gradually attracted attention with regard to understanding complex brain processing mechanisms. This study aims to comprehensively review and discuss the applications of Transformers in brain sciences. First, we present a brief introduction of the critical architecture of Transformers. Then, we overview and analyze their most relevant applications in brain sciences, including brain disease diagnosis, brain age prediction, brain anomaly detection, semantic segmentation, multi‐modal registration, functional Magnetic Resonance Imaging (fMRI) modeling, Electroencephalogram (EEG) processing, and multi‐task collaboration. We organize the model details and open sources for reference and replication. In addition, we discuss the quantitative assessments, model complexity, and optimization of Transformers, which are topics of great concern in the field. Finally, we explore possible future challenges and opportunities, exploiting some concrete and recent cases to provoke discussion and innovation. We hope that this review will stimulate interest in further research on Transformers in the context of brain sciences.

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Diagnosis of brain diseases in fusion of neuroimaging modalities using deep learning: A review

Cited by 43 publications

References 219 publications

Prognosis prediction in traumatic brain injury patients using machine learning algorithms

Prognosis prediction in traumatic brain injury patients using machine learning algorithms

Hybrid Deep Learning Approach for Accurate Tumor Detection in Medical Imaging Data

Understanding the brain with attention: A survey of transformers in brain sciences

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