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Mutation-Attention (MuAt): deep representation learning of somatic mutations for tumour ty** and subty**
BackgroundCancer genome sequencing enables accurate classification of tumours and tumour subtypes. However, prediction performance is still limited...
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Enhancing fall risk assessment: instrumenting vision with deep learning during walks
BackgroundFalls are common in a range of clinical cohorts, where routine risk assessment often comprises subjective visual observation only....
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Brain Age Prediction in Develo** Childhood with Multimodal Magnetic Resonance Images
It is well known that brain development is very fast and complex in the early childhood with age-based neurological and physiological changes of...
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How deep is the brain? The shallow brain hypothesis
Deep learning and predictive coding architectures commonly assume that inference in neural networks is hierarchical. However, largely neglected in...
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Deep learning networks based decision fusion model of EEG and fNIRS for classification of cognitive tasks
The detection of the cognitive tasks performed by a subject during data acquisition of a neuroimaging method has a wide range of applications:...
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A Multimodal Approach for Detection and Assessment of Depression Using Text, Audio and Video
Depression is one of the most common mental disorders, and rates of depression in individuals increase each year. Traditional diagnostic methods are...
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A multi-stage dynamical fusion network for multimodal emotion recognition
In recent years, emotion recognition using physiological signals has become a popular research topic. Physiological signal can reflect the real...
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Differential diagnosis of prostate cancer and benign prostatic hyperplasia based on DCE-MRI using bi-directional CLSTM deep learning and radiomics
Dynamic contrast-enhanced MRI (DCE-MRI) is routinely included in the prostate MRI protocol for a long time; its role has been questioned. It provides...
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Multimodal epigenetic sequencing analysis (MESA) of cell-free DNA for non-invasive colorectal cancer detection
BackgroundDetecting human cancers through cell-free DNA (cfDNA) in blood is a sensitive and non-invasive option. However, capturing multiple forms of...
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Harnessing multimodal data integration to advance precision oncology
Advances in quantitative biomarker development have accelerated new forms of data-driven insights for patients with cancer. However, most approaches...
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Quantitative and Visual Analysis of Data Augmentation and Hyperparameter Optimization in Deep Learning-Based Segmentation of Low-Grade Glioma Tumors Using Grad-CAM
This study executes a quantitative and visual investigation on the effectiveness of data augmentation and hyperparameter optimization on the accuracy...
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A Deep Learning-Based Automatic Collateral Assessment in Patients with Acute Ischemic Stroke
This study aimed to develop a supervised deep learning (DL) model for grading collateral status from dynamic susceptibility contrast magnetic...
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Predicting survival after radiosurgery in patients with lung cancer brain metastases using deep learning of radiomics and EGFR status
The early prediction of overall survival (OS) in patients with lung cancer brain metastases (BMs) after Gamma Knife radiosurgery (GKRS) can...
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Machine learning integrative approaches to advance computational immunology
The study of immunology, traditionally reliant on proteomics to evaluate individual immune cells, has been revolutionized by single-cell RNA...
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mResU-Net: multi-scale residual U-Net-based brain tumor segmentation from multimodal MRI
Brain tumor segmentation is an important direction in medical image processing, and its main goal is to accurately mark the tumor part in brain MRI....
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DeepMPF: deep learning framework for predicting drug–target interactions based on multi-modal representation with meta-path semantic analysis
BackgroundDrug-target interaction (DTI) prediction has become a crucial prerequisite in drug design and drug discovery. However, the traditional...
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Towards Improving Motor Imagery Brain–Computer Interface Using Multimodal Speech Imagery
PurposeThe brain–computer interface (BCI) based on motor imagery (MI) has attracted extensive interest due to its spontaneity and convenience....
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Image Registration: Fundamentals and Recent Advances Based on Deep Learning
Registration is the process of establishing spatial correspondences between images. It allows for the alignment and transfer of key information...
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When deep learning is not enough: artificial life as a supplementary tool for segmentation of ultrasound images of breast cancer
Segmentation of tumors in ultrasound (US) images of the breast is a critical issue in medical imaging. Due to the poor quality of US images and the...
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Real-time detection of laryngopharyngeal cancer using an artificial intelligence-assisted system with multimodal data
BackgroundLaryngopharyngeal cancer (LPC) includes laryngeal and hypopharyngeal cancer, whose early diagnosis can significantly improve the prognosis...
