AIMC Topic: Deep Learning

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Deep Learning: A Breakthrough in Medical Imaging.

Current medical imaging
Deep learning has attracted great attention in the medical imaging community as a promising solution for automated, fast and accurate medical image analysis, which is mandatory for quality healthcare. Convolutional neural networks and its variants ha...

Deep Learning for Acute Myeloid Leukemia Diagnosis.

Journal of medicine and life
By changing the lifestyle and increasing the cancer incidence, accurate diagnosis becomes a significant medical action. Today, DNA microarray is widely used in cancer diagnosis and screening since it is able to measure gene expression levels. Analyzi...

Automated Intensity Modulated Radiation Therapy Treatment Planning for Cervical Cancer Based on Convolution Neural Network.

Technology in cancer research & treatment
PURPOSE: To develop and evaluate an automatic intensity-modulated radiation therapy (IMRT) program for cervical cancer, including a Convolution Neural Network (CNN)-based prediction model and an automated optimization strategy.

Quick and accurate selection of hand images among radiographs from various body parts using deep learning.

Journal of X-ray science and technology
BACKGROUND: Although rheumatoid arthritis (RA) causes destruction of articular cartilage, early treatment significantly improves symptoms and delays progression. It is important to detect subtle damage for an early diagnosis. Recent software programs...

Deep Learning for Carotid Plaque Segmentation using a Dilated U-Net Architecture.

Ultrasonic imaging
Carotid plaque segmentation in ultrasound longitudinal B-mode images using deep learning is presented in this work. We report on 101 severely stenotic carotid plaque patients. A standard U-Net is compared with a dilated U-Net architecture in which th...

Detection of coronavirus disease from X-ray images using deep learning and transfer learning algorithms.

Journal of X-ray science and technology
OBJECTIVE: This study aims to employ the advantages of computer vision and medical image analysis to develop an automated model that has the clinical potential for early detection of novel coronavirus (COVID-19) infected disease.

Radiomics and artificial Intelligence for PET imaging analysis.

Nuclear medicine review. Central & Eastern Europe
In recent years, processing of the imaging signal derived from CT, MR or positron emission has proven to be able to predict outcome parameters in cancer patients. The processing techniques of the signal constitute the discipline of radiomics. The qua...

Identification of COVID-19 samples from chest X-Ray images using deep learning: A comparison of transfer learning approaches.

Journal of X-ray science and technology
BACKGROUND: The novel coronavirus disease 2019 (COVID-19) constitutes a public health emergency globally. The number of infected people and deaths are proliferating every day, which is putting tremendous pressure on our social and healthcare system. ...

Electroencephalogram-Based Emotion Recognition Using a Particle Swarm Optimization-Derived Support Vector Machine Classifier.

Critical reviews in biomedical engineering
We sort human emotions using Russell's circumplex model of emotion by classifying electroencephalogram (EEG) signals from 25 subjects into four discrete states, namely, happy, sad, angry, and relaxed. After acquiring signals, we use a standard databa...

Automated Gleason grading of prostate cancer using transfer learning from general-purpose deep-learning networks.

Romanian journal of morphology and embryology = Revue roumaine de morphologie et embryologie
Two deep-learning algorithms designed to classify images according to the Gleason grading system that used transfer learning from two well-known general-purpose image classification networks (AlexNet and GoogleNet) were trained on Hematoxylin-Eosin h...