AIMC Topic: Neural Networks, Computer

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Which data subset should be augmented for deep learning? a simulation study using urothelial cell carcinoma histopathology images.

BMC bioinformatics
BACKGROUND: Applying deep learning to digital histopathology is hindered by the scarcity of manually annotated datasets. While data augmentation can ameliorate this obstacle, its methods are far from standardized. Our aim was to systematically explor...

Formulation and Characterization of Buccal Films Containing Valsartan with Additional Support from Image Analysis.

AAPS PharmSciTech
The present study was aimed to the development and characterization of valsartan-containing buccal films with an introduction to a novel technique of image analysis. Visual inspection of the film provided a wealth of information that was difficult to...

Computed tomography-based COVID-19 triage through a deep neural network using mask-weighted global average pooling.

Frontiers in cellular and infection microbiology
BACKGROUND: There is an urgent need to find an effective and accurate method for triaging coronavirus disease 2019 (COVID-19) patients from millions or billions of people. Therefore, this study aimed to develop a novel deep-learning approach for COVI...

A Two-Stage End-to-End Deep Learning Framework for Pathologic Examination in Skin Tumor Diagnosis.

The American journal of pathology
Neurofibromas (NFs), Bowen disease (BD), and seborrheic keratosis (SK) are common skin tumors. Pathologic examination is the gold standard for diagnosis of these tumors. Current pathologic diagnosis is primarily based on microscopic observation, whic...

Multi-View Human Action Recognition Using Skeleton Based-FineKNN with Extraneous Frame Scrapping Technique.

Sensors (Basel, Switzerland)
Human action recognition (HAR) is one of the most active research topics in the field of computer vision. Even though this area is well-researched, HAR algorithms such as 3D Convolution Neural Networks (CNN), Two-stream Networks, and CNN-LSTM (Long S...

Decoding behavior from global cerebrovascular activity using neural networks.

Scientific reports
Functional Ultrasound (fUS) provides spatial and temporal frames of the vascular activity in the brain with high resolution and sensitivity in behaving animals. The large amount of resulting data is underused at present due to the lack of appropriate...

Devising a deep neural network based mammography phantom image filtering algorithm using images obtained under mAs and kVp control.

Scientific reports
We study whether deep neural network based algorithm can filter out mammography phantom images that will pass or fail. With 543 phantom images generated from a mammography unit, we created VGG16 based phantom shape scoring models (multi-and binary-cl...

MNAS: Multi-Scale and Multi-Level Memory-Efficient Neural Architecture Search for Low-Dose CT Denoising.

IEEE transactions on medical imaging
Lowering the radiation dose in computed tomography (CT) can greatly reduce the potential risk to public health. However, the reconstructed images from dose-reduced CT or low-dose CT (LDCT) suffer from severe noise which compromises the subsequent dia...

IPC prediction of patent documents using neural network with attention for hierarchical structure.

PloS one
International patent classifications (IPCs) are assigned to patent documents; however, since the procedure for assigning classifications is manually done by the patent examiner, it takes a lot of time and effort to select some IPCs from about 70,000 ...

Deep learning based MRI reconstruction with transformer.

Computer methods and programs in biomedicine
Magnetic resonance imaging (MRI) has become one of the most powerful imaging techniques in medical diagnosis, yet the prolonged scanning time becomes a bottleneck for application. Reconstruction methods based on compress sensing (CS) have made progre...