AIMC Topic: Deep Learning

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Deep learning detector for high precision monitoring of cell encapsulation statistics in microfluidic droplets.

Lab on a chip
Encapsulation of cells inside microfluidic droplets is central to several applications involving cellular analysis. Although, theoretically the encapsulation statistics are expected to follow a Poisson distribution, experimentally this may not be ach...

D-CryptO: deep learning-based analysis of colon organoid morphology from brightfield images.

Lab on a chip
Stem cell-derived organoids are a promising tool to model native human tissues as they resemble human organs functionally and structurally compared to traditional monolayer cell-based assays. For instance, colon organoids can spontaneously develop cr...

Development, validation, and feature extraction of a deep learning model predicting in-hospital mortality using Japan's largest national ICU database: a validation framework for transparent clinical Artificial Intelligence (cAI) development.

Anaesthesia, critical care & pain medicine
OBJECTIVE: While clinical Artificial Intelligence (cAI) mortality prediction models and relevant studies have increased, limitations including the lack of external validation studies and inadequate model calibration leading to decreased overall accur...

Pandemic disease detection through wireless communication using infrared image based on deep learning.

Mathematical biosciences and engineering : MBE
Rapid diagnosis to test diseases, such as COVID-19, is a significant issue. It is a routine virus test in a reverse transcriptase-polymerase chain reaction. However, a test like this takes longer to complete because it follows the serial testing meth...

LangMoDHS: A deep learning language model for predicting DNase I hypersensitive sites in mouse genome.

Mathematical biosciences and engineering : MBE
DNase I hypersensitive sites (DHSs) are a specific genomic region, which is critical to detect or understand cis-regulatory elements. Although there are many methods developed to detect DHSs, there is a big gap in practice. We presented a deep learni...

Strong semantic segmentation for Covid-19 detection: Evaluating the use of deep learning models as a performant tool in radiography.

Radiography (London, England : 1995)
INTRODUCTION: With the increasing number of Covid-19 cases as well as care costs, chest diseases have gained increasing interest in several communities, particularly in medical and computer vision. Clinical and analytical exams are widely recognized ...

An automated unsupervised deep learning-based approach for diabetic retinopathy detection.

Medical & biological engineering & computing
As per the International Diabetes Federation (IDF) report, 35-60% of people suffering from diabetic retinopathy (DR) have a history of diabetes. DR is one of the primary reasons for blindness and visual impairment worldwide among adults aged 24-74 ye...

Deep Learning-Based Synthesized View Quality Enhancement with DIBR Distortion Mask Prediction Using Synthetic Images.

Sensors (Basel, Switzerland)
Recently, deep learning-based image quality enhancement models have been proposed to improve the perceptual quality of distorted synthesized views impaired by compression and the Depth Image-Based Rendering (DIBR) process in a multi-view video system...

Survival analysis of localized prostate cancer with deep learning.

Scientific reports
In recent years, data-driven, deep-learning-based models have shown great promise in medical risk prediction. By utilizing the large-scale Electronic Health Record data found in the U.S. Department of Veterans Affairs, the largest integrated healthca...

A Deep Learning-Based Computer Aided Detection (CAD) System for Difficult-to-Detect Brain Metastases.

International journal of radiation oncology, biology, physics
PURPOSE: We sought to develop a computer-aided detection (CAD) system that optimally augments human performance, excelling especially at identifying small inconspicuous brain metastases (BMs), by training a convolutional neural network on a unique ma...