AIMC Topic: Deep Learning

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Subject-Specific Cognitive Workload Classification Using EEG-Based Functional Connectivity and Deep Learning.

Sensors (Basel, Switzerland)
Cognitive workload is a crucial factor in tasks involving dynamic decision-making and other real-time and high-risk situations. Neuroimaging techniques have long been used for estimating cognitive workload. Given the portability, cost-effectiveness a...

Predicting microvascular invasion in hepatocellular carcinoma: a deep learning model validated across hospitals.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: The accuracy of estimating microvascular invasion (MVI) preoperatively in hepatocellular carcinoma (HCC) by clinical observers is low. Most recent studies constructed MVI predictive models utilizing radiological and/or radiomics features ...

Application of deep learning neural network in predicting bone mineral density from plain X-ray radiography.

Archives of osteoporosis
UNLABELLED: DeepDXA is a deep learning model designed to infer bone mineral density data from plain pelvis X-ray, and it can achieve good predicted value for clinical use.

Deep Learning for nasopharyngeal Carcinoma Identification Using Both White Light and Narrow-Band Imaging Endoscopy.

The Laryngoscope
OBJECTIVES/HYPOTHESIS: To develop a deep-learning-based automatic diagnosis system for identifying nasopharyngeal carcinoma (NPC) from noncancer (inflammation and hyperplasia), using both white light imaging (WLI) and narrow-band imaging (NBI) nasoph...

Deep Learning-Based Recurrence Prediction of Atrial Fibrillation After Catheter Ablation.

Circulation journal : official journal of the Japanese Circulation Society
BACKGROUND: Radiofrequency catheter ablation (RFCA) is an effective therapy for atrial fibrillation (AF). However, it the problem of AF recurrence remains. This study investigates whether a deep convolutional neural network (CNN) can accurately predi...

Deep learning-based classification and structure name standardization for organ at risk and target delineations in prostate cancer radiotherapy.

Journal of applied clinical medical physics
Radiotherapy (RT) datasets can suffer from variations in annotation of organ at risk (OAR) and target structures. Annotation standards exist, but their description for prostate targets is limited. This restricts the use of such data for supervised ma...

Revisiting the out of Africa event with a deep-learning approach.

American journal of human genetics
Anatomically modern humans evolved around 300 thousand years ago in Africa. They started to appear in the fossil record outside of Africa as early as 100 thousand years ago, although other hominins existed throughout Eurasia much earlier. Recently, s...

Annotation-efficient deep learning for automatic medical image segmentation.

Nature communications
Automatic medical image segmentation plays a critical role in scientific research and medical care. Existing high-performance deep learning methods typically rely on large training datasets with high-quality manual annotations, which are difficult to...

Protein Family Classification from Scratch: A CNN Based Deep Learning Approach.

IEEE/ACM transactions on computational biology and bioinformatics
Next-generation sequencing techniques provide us with an opportunity for generating sequenced proteins and identifying the biological families and functions of these proteins. However, compared with identified proteins, uncharacterized proteins consi...

A review for cell and particle tracking on microscopy images using algorithms and deep learning technologies.

Biomedical journal
Time-lapse microscopy images generated by biological experiments have been widely used for observing target activities, such as the motion trajectories and survival states. Based on these observations, biologists can conclude experimental results or ...