AIMC Topic: Deep Learning

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A Fault Prediction and Cause Identification Approach in Complex Industrial Processes Based on Deep Learning.

Computational intelligence and neuroscience
Faults occurring in the production line can cause many losses. Predicting the fault events before they occur or identifying the causes can effectively reduce such losses. A modern production line can provide enough data to solve the problem. However,...

Application of an emotional classification model in e-commerce text based on an improved transformer model.

PloS one
With the rapid development of the mobile internet, people are becoming more dependent on the internet to express their comments on products or stores; meanwhile, text sentiment classification of these comments has become a research hotspot. In existi...

DeepAlign, a 3D alignment method based on regionalized deep learning for Cryo-EM.

Journal of structural biology
Cryo Electron Microscopy (Cryo-EM) is currently one of the main tools to reveal the structural information of biological specimens at high resolution. Despite the great development of the techniques involved to solve the biological structures with Cr...

MRI-Based Deep-Learning Method for Determining Glioma Promoter Methylation Status.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: () promoter methylation confers an improved prognosis and treatment response in gliomas. We developed a deep learning network for determining promoter methylation status using T2 weighted Images (T2WI) only.

Computer-aided diagnosis of ground glass pulmonary nodule by fusing deep learning and radiomics features.

Physics in medicine and biology
OBJECTIVES: This study aims to develop a computer-aided diagnosis (CADx) scheme to classify between benign and malignant ground glass nodules (GGNs), and fuse deep leaning and radiomics imaging features to improve the classification performance.

Deep learning with a convolutional neural network model to differentiate renal parenchymal tumors: a preliminary study.

Abdominal radiology (New York)
PURPOSE: With advancements in medical imaging, more renal tumors are detected early, but it remains a challenge for radiologists to accurately distinguish subtypes of renal parenchymal tumors. We aimed to establish a novel deep convolutional neural n...

Clinical implementation of deep learning contour autosegmentation for prostate radiotherapy.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: Artificial intelligence advances have stimulated a new generation of autosegmentation, however clinical evaluations of these algorithms are lacking. This study assesses the clinical utility of deep learning-based autosegmentat...

HistoNet: A Deep Learning-Based Model of Normal Histology.

Toxicologic pathology
We introduce HistoNet, a deep neural network trained on normal tissue. On 1690 slides with rat tissue samples from 6 preclinical toxicology studies, tissue regions were outlined and annotated by pathologists into 46 different tissue classes. From the...

Prediction of antibiotic-resistance genes occurrence at a recreational beach with deep learning models.

Water research
Antibiotic resistance genes (ARGs) have been reported to threaten the public health of beachgoers worldwide. Although ARG monitoring and beach guidelines are necessary, substantial efforts are required for ARG sampling and analysis. Accordingly, in t...

Whole-slide margin control through deep learning in Mohs micrographic surgery for basal cell carcinoma.

Experimental dermatology
BACKGROUND: Basal cell carcinoma (BCC) is the most common type of skin cancer with incidence rates rising each year. Mohs micrographic surgery (MMS) is most often chosen as treatment for BCC on the face for which each frozen section has to be histolo...