AIMC Topic: Deep Learning

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Identification of adaptor proteins by incorporating deep learning and PSSM profiles.

Methods (San Diego, Calif.)
Adaptor proteins, also known as signal transduction adaptor proteins, are important proteins in signal transduction pathways, and play a role in connecting signal proteins for signal transduction between cells. Studies have shown that adaptor protein...

Virtual Impactor-Based Label-Free Pollen Detection using Holography and Deep Learning.

ACS sensors
Exposure to bio-aerosols such as pollen can lead to adverse health effects. There is a need for a portable and cost-effective device for long-term monitoring and quantification of various types of pollen. To address this need, we present a mobile and...

Silent Speech Recognition with Strain Sensors and Deep Learning Analysis of Directional Facial Muscle Movement.

ACS applied materials & interfaces
Silent communication based on biosignals from facial muscle requires accurate detection of its directional movement and thus optimally positioning minimum numbers of sensors for higher accuracy of speech recognition with a minimal person-to-person va...

Development and performance evaluation of a deep learning lung nodule detection system.

BMC medical imaging
BACKGROUND: Lung cancer is the leading cause of cancer-related deaths throughout the world. Chest computed tomography (CT) is now widely used in the screening and diagnosis of lung cancer due to its effectiveness. Radiologists must identify each smal...

Deep learning-assisted sensitive detection of fentanyl using a bubbling-microchip.

Lab on a chip
Deep learning-enabled smartphone-based image processing has significant advantages in the development of point-of-care diagnostics. Conventionally, most deep-learning applications require task specific large scale expertly annotated datasets. Therefo...

A multistage multimodal deep learning model for disease severity assessment and early warnings of high-risk patients of COVID-19.

Frontiers in public health
The outbreak of coronavirus disease 2019 (COVID-19) has caused massive infections and large death tolls worldwide. Despite many studies on the clinical characteristics and the treatment plans of COVID-19, they rarely conduct in-depth prognostic resea...

Deep Learning Model for the Image Fusion and Accurate Classification of Remote Sensing Images.

Computational intelligence and neuroscience
Deep learning is widely used for the classification of images that have various attributes. Image data are used to extract colour, texture, form, and local features. These features are combined in feature-level image fusion to create a merged remote ...

A multidomain fusion model of radiomics and deep learning to discriminate between PDAC and AIP based on F-FDG PET/CT images.

Japanese journal of radiology
PURPOSE: To explore a multidomain fusion model of radiomics and deep learning features based on F-fluorodeoxyglucose positron emission tomography/computed tomography (F-FDG PET/CT) images to distinguish pancreatic ductal adenocarcinoma (PDAC) and aut...

Where Position Matters-Deep-Learning-Driven Normalization and Coregistration of Computed Tomography in the Postoperative Analysis of Deep Brain Stimulation.

Neuromodulation : journal of the International Neuromodulation Society
INTRODUCTION: Recent developments in the postoperative evaluation of deep brain stimulation surgery on the group level warrant the detection of achieved electrode positions based on postoperative imaging. Computed tomography (CT) is a frequently used...

Predicting clinically significant prostate cancer with a deep learning approach: a multicentre retrospective study.

European journal of nuclear medicine and molecular imaging
PURPOSE: This study aimed to develop deep learning (DL) models based on multicentre biparametric magnetic resonance imaging (bpMRI) for the diagnosis of clinically significant prostate cancer (csPCa) and compare the performance of these models with t...