AIMC Topic: Deep Learning

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Deep-RBPPred: Predicting RNA binding proteins in the proteome scale based on deep learning.

Scientific reports
RNA binding protein (RBP) plays an important role in cellular processes. Identifying RBPs by computation and experiment are both essential. Recently, an RBP predictor, RBPPred, is proposed in our group to predict RBPs. However, RBPPred is too slow fo...

Validation of deep-learning-based triage and acuity score using a large national dataset.

PloS one
AIM: Triage is important in identifying high-risk patients amongst many less urgent patients as emergency department (ED) overcrowding has become a national crisis recently. This study aims to validate that a Deep-learning-based Triage and Acuity Sco...

Single-sequence-based prediction of protein secondary structures and solvent accessibility by deep whole-sequence learning.

Journal of computational chemistry
Predicting protein structure from sequence alone is challenging. Thus, the majority of methods for protein structure prediction rely on evolutionary information from multiple sequence alignments. In previous work we showed that Long Short-Term Bidire...

Enlarged perivascular spaces in brain MRI: Automated quantification in four regions.

NeuroImage
Enlarged perivascular spaces (PVS) are structural brain changes visible in MRI, are common in aging, and are considered a reflection of cerebral small vessel disease. As such, assessing the burden of PVS has promise as a brain imaging marker. Visual ...

Deep Geodesic Learning for Segmentation and Anatomical Landmarking.

IEEE transactions on medical imaging
In this paper, we propose a novel deep learning framework for anatomy segmentation and automatic landmarking. Specifically, we focus on the challenging problem of mandible segmentation from cone-beam computed tomography (CBCT) scans and identificatio...

Generative Adversarial Network for Medical Images (MI-GAN).

Journal of medical systems
Deep learning algorithms produces state-of-the-art results for different machine learning and computer vision tasks. To perform well on a given task, these algorithms require large dataset for training. However, deep learning algorithms lack generali...

Machine and deep learning for sport-specific movement recognition: a systematic review of model development and performance.

Journal of sports sciences
Objective assessment of an athlete's performance is of importance in elite sports to facilitate detailed analysis. The implementation of automated detection and recognition of sport-specific movements overcomes the limitations associated with manual ...

Using deep autoencoders to identify abnormal brain structural patterns in neuropsychiatric disorders: A large-scale multi-sample study.

Human brain mapping
Machine learning is becoming an increasingly popular approach for investigating spatially distributed and subtle neuroanatomical alterations in brain-based disorders. However, some machine learning models have been criticized for requiring a large nu...

Retinal image quality assessment using deep learning.

Computers in biology and medicine
Poor-quality retinal images do not allow an accurate medical diagnosis, and it is inconvenient for a patient to return to a medical center to repeat the fundus photography exam. In this paper, a robust automatic system is proposed to assess the quali...