Advances in Data Preprocessing for Biomedical Data Fusion: An Overview of the Methods, Challenges, and Prospects

Wang, Shuihua; Çelebi, Mehmet; Zhang, Yudong; Yu, Xi; Lu, Siyuan; Yao, Xujing; Zhou, Qinghua; Martínez-García, Miguel; Tian, Yingli; Górriz, Juan Manuel; Tyukin, Ivan

doi:10.1016/j.inffus.2021.07.001

Cited by 155 publications

(61 citation statements)

References 380 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, deep learning has emerged as a powerful tool for medical image analysis. With the development of image processing techniques, deep learning methods are beginning to show increasing advantages over humans in various fields such as denoising, feature extraction and dimensionality reduction of medical images [ 30 ]. Previous studies have achieved high performance with deep learning methods for tumor diagnosis based on radiomics data [ 31 ].…”

Section: Discussionmentioning

confidence: 99%

Deep Learning Predicts EBV Status in Gastric Cancer Based on Spatial Patterns of Lymphocyte Infiltration

Zhang

Yao

et al. 2021

Cancers

View full text Add to dashboard Cite

EBV infection occurs in around 10% of gastric cancer cases and represents a distinct subtype, characterized by a unique mutation profile, hypermethylation, and overexpression of PD-L1. Moreover, EBV positive gastric cancer tends to have higher immune infiltration and a better prognosis. EBV infection status in gastric cancer is most commonly determined using PCR and in situ hybridization, but such a method requires good nucleic acid preservation. Detection of EBV status with histopathology images may complement PCR and in situ hybridization as a first step of EBV infection assessment. Here, we developed a deep learning-based algorithm to directly predict EBV infection in gastric cancer from H&E stained histopathology slides. Our model can not only predict EBV infection in gastric cancers from tumor regions but also from normal regions with potential changes induced by adjacent EBV+ regions within each H&E slide. Furthermore, in cohorts with zero EBV abundances, a significant difference of immune infiltration between high and low EBV score samples was observed, consistent with the immune infiltration difference observed between EBV positive and negative samples. Therefore, we hypothesized that our model’s prediction of EBV infection is partially driven by the spatial information of immune cell composition, which was supported by mostly positive local correlations between the EBV score and immune infiltration in both tumor and normal regions across all H&E slides. Finally, EBV scores calculated from our model were found to be significantly associated with prognosis. This framework can be readily applied to develop interpretable models for prediction of virus infection across cancers.

show abstract

Section: Discussionmentioning

confidence: 99%

Deep Learning Predicts EBV Status in Gastric Cancer Based on Spatial Patterns of Lymphocyte Infiltration

Zhang

Yao

et al. 2021

Cancers

View full text Add to dashboard Cite

show abstract

“…As a result, up to 72.97% (1,741 out of 2,386) samples do not have consistent results between tests and are grouped into uncertain cases while using only three tests results as classification inputs showed poor diagnose validity, and also cannot reach the diagnosing capacity that the DL framework has achieved ( Supplementary Table 2 ). This proved the necessity of data fusion ( Zhang et al, 2020 ; Wang et al, 2021 ) between neuropsychological tests and neuroimages, such as FDG-PET or MRI, to certainly diagnose AD-related neurodegeneration ( McKhann et al, 2011 ; Zhang et al, 2018 ).…”

Section: Discussionmentioning

confidence: 99%

“…It abstractly extracts high-dimensional features along with a powerful classification ability and does not rely on expert-designed features such as traditional methods (e.g., linear regression and support vector machine). For diagnosing AD and related pathology, the neurodegeneration revealed by FDG-PET hypometabolism and atrophy on MRI are both defined as multimodal biomarkers ( Jack et al, 2018 ; Zhang et al, 2020 ; Wang et al, 2021 ). The diagnosis ( Ortiz et al, 2016 ) or prediction ( Shen et al, 2019 ; Spasov et al, 2019 ) based on deep neural networks was proposed and showed high accuracy with fast implementation.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning With 18F-Fluorodeoxyglucose-PET Gives Valid Diagnoses for the Uncertain Cases in Memory Impairment of Alzheimer’s Disease

Zhang

Pan

et al. 2021

Front. Aging Neurosci.

View full text Add to dashboard Cite

Objectives: Neuropsychological tests are an important basis for the memory impairment diagnosis in Alzheimer’s disease (AD). However, multiple memory tests might be conflicting within-subjects and lead to uncertain diagnoses in some cases. This study proposed a framework to diagnose the uncertain cases of memory impairment.Methods: We collected 2,386 samples including AD, mild cognitive impairment (MCI), and cognitive normal (CN) using 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) and three different neuropsychological tests (Mini-Mental State Examination, Alzheimer’s Disease Assessment Scale-Cognitive Subscale, and Clinical Dementia Rating) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). A deep learning (DL) framework using FDG-PET was proposed to diagnose uncertain memory impairment cases that were conflicting between tests. Subsequent ANOVA, chi-squared, and t-test were used to explain the potential causes of uncertain cases.Results: For certain cases in the testing set, the proposed DL framework outperformed other methods with 95.65% accuracy. For the uncertain cases, its positive diagnoses had a significant (p < 0.001) worse decline in memory function than negative diagnoses in a longitudinal study of 40 months on average. In the memory-impaired group, uncertain cases were mainly explained by an AD metabolism pattern but mild in extent (p < 0.05). In the healthy group, uncertain cases were mainly explained by a non-energetic mental state (p < 0.001) measured using a global deterioration scale (GDS), with a significant depression-related metabolism pattern detected (p < 0.05).Conclusion: A DL framework for diagnosing uncertain cases of memory impairment is proposed. Proved by longitudinal tracing of its diagnoses, it showed clinical validity and had application potential. Its valid diagnoses also provided evidence and explanation of uncertain cases based on the neurodegeneration and depression mental state.

show abstract

“…The most recent research in computer vision applied deep learning for human gait recognition with better recognition accuracy [31]. The researchers used deep learning performance in varying domains such as object identification, disease segmentation, and recognition from photographs and videos [32][33][34][35]. The convolution neural network (CNN) is a type of deep learning, which includes several layers that extract the deep features of each image [36,37].…”

Section: Introductionmentioning

confidence: 99%

Human Gait Recognition: A Single Stream Optimal Deep Learning Features Fusion

Saleem

Khan

Alhaisoni

et al. 2021

Sensors

View full text Add to dashboard Cite

Human Gait Recognition (HGR) is a biometric technique that has been utilized for security purposes for the last decade. The performance of gait recognition can be influenced by various factors such as wearing clothes, carrying a bag, and the walking surfaces. Furthermore, identification from differing views is a significant difficulty in HGR. Many techniques have been introduced in the literature for HGR using conventional and deep learning techniques. However, the traditional methods are not suitable for large datasets. Therefore, a new framework is proposed for human gait recognition using deep learning and best feature selection. The proposed framework includes data augmentation, feature extraction, feature selection, feature fusion, and classification. In the augmentation step, three flip operations were used. In the feature extraction step, two pre-trained models were employed, Inception-ResNet-V2 and NASNet Mobile. Both models were fine-tuned and trained using transfer learning on the CASIA B gait dataset. The features of the selected deep models were optimized using a modified three-step whale optimization algorithm and the best features were chosen. The selected best features were fused using the modified mean absolute deviation extended serial fusion (MDeSF) approach. Then, the final classification was performed using several classification algorithms. The experimental process was conducted on the entire CASIA B dataset and achieved an average accuracy of 89.0. Comparison with existing techniques showed an improvement in accuracy, recall rate, and computational time.

show abstract

Advances in Data Preprocessing for Biomedical Data Fusion: An Overview of the Methods, Challenges, and Prospects

Cited by 155 publications

References 380 publications

Deep Learning Predicts EBV Status in Gastric Cancer Based on Spatial Patterns of Lymphocyte Infiltration

Deep Learning Predicts EBV Status in Gastric Cancer Based on Spatial Patterns of Lymphocyte Infiltration

Deep Learning With 18F-Fluorodeoxyglucose-PET Gives Valid Diagnoses for the Uncertain Cases in Memory Impairment of Alzheimer’s Disease

Human Gait Recognition: A Single Stream Optimal Deep Learning Features Fusion

Contact Info

Product

Resources

About