AIMC Topic: Deep Learning

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A Regularized Deep Learning Approach for Clinical Risk Prediction of Acute Coronary Syndrome Using Electronic Health Records.

IEEE transactions on bio-medical engineering
OBJECTIVE: Acute coronary syndrome (ACS), as a common and severe cardiovascular disease, is a leading cause of death and the principal cause of serious long-term disability globally. Clinical risk prediction of ACS is important for early intervention...

An Automatic Detection System of Lung Nodule Based on Multigroup Patch-Based Deep Learning Network.

IEEE journal of biomedical and health informatics
High-efficiency lung nodule detection dramatically contributes to the risk assessment of lung cancer. It is a significant and challenging task to quickly locate the exact positions of lung nodules. Extensive work has been done by researchers around t...

Deep Belief Networks for Electroencephalography: A Review of Recent Contributions and Future Outlooks.

IEEE journal of biomedical and health informatics
Deep learning, a relatively new branch of machine learning, has been investigated for use in a variety of biomedical applications. Deep learning algorithms have been used to analyze different physiological signals and gain a better understanding of h...

Deep Learning based Radiomics (DLR) and its usage in noninvasive IDH1 prediction for low grade glioma.

Scientific reports
Deep learning-based radiomics (DLR) was developed to extract deep information from multiple modalities of magnetic resonance (MR) images. The performance of DLR for predicting the mutation status of isocitrate dehydrogenase 1 (IDH1) was validated in ...

Deep Learning for Fully-Automated Localization and Segmentation of Rectal Cancer on Multiparametric MR.

Scientific reports
Multiparametric Magnetic Resonance Imaging (MRI) can provide detailed information of the physical characteristics of rectum tumours. Several investigations suggest that volumetric analyses on anatomical and functional MRI contain clinically valuable ...

Deep Learning- and Transfer Learning-Based Super Resolution Reconstruction from Single Medical Image.

Journal of healthcare engineering
Medical images play an important role in medical diagnosis and research. In this paper, a transfer learning- and deep learning-based super resolution reconstruction method is introduced. The proposed method contains one bicubic interpolation template...

Breast Cancer Multi-classification from Histopathological Images with Structured Deep Learning Model.

Scientific reports
Automated breast cancer multi-classification from histopathological images plays a key role in computer-aided breast cancer diagnosis or prognosis. Breast cancer multi-classification is to identify subordinate classes of breast cancer (Ductal carcino...

Modeling Task fMRI Data Via Deep Convolutional Autoencoder.

IEEE transactions on medical imaging
Task-based functional magnetic resonance imaging (tfMRI) has been widely used to study functional brain networks under task performance. Modeling tfMRI data is challenging due to at least two problems: the lack of the ground truth of underlying neura...

An Advanced Deep Learning Approach for Ki-67 Stained Hotspot Detection and Proliferation Rate Scoring for Prognostic Evaluation of Breast Cancer.

Scientific reports
Being a non-histone protein, Ki-67 is one of the essential biomarkers for the immunohistochemical assessment of proliferation rate in breast cancer screening and grading. The Ki-67 signature is always sensitive to radiotherapy and chemotherapy. Due t...

Protein-Protein Interaction Interface Residue Pair Prediction Based on Deep Learning Architecture.

IEEE/ACM transactions on computational biology and bioinformatics
MOTIVATION: Proteins usually fulfill their biological functions by interacting with other proteins. Although some methods have been developed to predict the binding sites of a monomer protein, these are not sufficient for prediction of the interactio...