Search Results for author:
Found 20 papers, 0 papers with code
Kinematics Modeling of Peroxy Free Radicals: A Deep Reinforcement Learning Approach
Tropospheric ozone, known as a concerning air pollutant, has been associated with health issues including asthma, bronchitis, and impaired lung function.
Uncertainty Quantification in Detecting Choroidal Metastases on MRI via Evolutionary Strategies
Given the challenges associated with acquiring large, annotated datasets in this field, there is a need for methods that enable uncertainty quantification in small data AI approaches tailored to radiology images.
Deep neuroevolution to predict primary brain tumor grade from functional MRI adjacency matrices
Whereas MRI produces anatomic information about the brain, functional MRI (fMRI) tells us about neural activity within the brain, including how various regions communicate with each other.
Deep neuroevolution for limited, heterogeneous data: proof-of-concept application to Neuroblastoma brain metastasis using a small virtual pooled image collection
As in prior DNE work, we used a small training set, consisting of 30 normal and 30 metastasis-containing post-contrast MRI brain scans, with 37% outside images.
Deep reinforcement learning for fMRI prediction of Autism Spectrum Disorder
We hypothesized that a Deep Reinforcement Learning (DRL) classifier could learn effectively on a small fMRI training set.
Direct evaluation of progression or regression of disease burden in brain metastatic disease with Deep Neuroevolution
We applied the CNNs to our training set, as well as a separate testing set with the same class balance of 25 progression and 25 regression images.
Deep Neuroevolution Squeezes More out of Small Neural Networks and Small Training Sets: Sample Application to MRI Brain Sequence Classification
Purpose: Deep Neuroevolution (DNE) holds the promise of providing radiology artificial intelligence (AI) that performs well with small neural networks and small training sets.
Efficient Feature Representations for Cricket Data Analysis using Deep Learning based Multi-Modal Fusion Model
To naturally facilitate the learning of meaningful representations of features for accurate data analysis, we formulate a deep representation learning framework which jointly learns a custom set of embeddings (which represents our features of interest) through the minimization of a contrastive loss.
Deep reinforcement learning with automated label extraction from clinical reports accurately classifies 3D MRI brain volumes
Part 2: Then, using these labels, whereas the supervised approach quickly overfit the training data and as expected performed poorly on the testing set (66% accuracy, just over random guessing), the reinforcement learning approach achieved an accuracy of 92%.
Deep Neural Network Based Differential Equation Solver for HIV Enzyme Kinetics
Purpose: We seek to use neural networks (NNs) to solve a well-known system of differential equations describing the balance between T cells and HIV viral burden.
Deep reinforcement learning-based image classification achieves perfect testing set accuracy for MRI brain tumors with a training set of only 30 images
We achieved perfect testing set accuracy with a training set of merely 30 images.
Unsupervised deep clustering and reinforcement learning can accurately segment MRI brain tumors with very small training sets
Materials and Methods: We initially clustered images using unsupervised deep learning clustering to generate candidate lesion masks for each MRI image.
EMRs with Blockchain : A distributed democratised Electronic Medical Record sharing platform
Medical data sharing needs to be done with the utmost respect for privacy and security.
Cryptography and Security
Multi-Modal Detection of Alzheimer's Disease from Speech and Text
Reliable detection of the prodromal stages of Alzheimer's disease (AD) remains difficult even today because, unlike other neurocognitive impairments, there is no definitive diagnosis of AD in vivo.
Depression Status Estimation by Deep Learning based Hybrid Multi-Modal Fusion Model
Due to the immense variations in character level traits from person to person, traditional deep learning methods fail to generalize in a real world setting.
A Novel Sentiment Analysis Engine for Preliminary Depression Status Estimation on Social Media
We propose a cloud-based smartphone application, with a deep learning-based backend to primarily perform depression detection on Twitter social media.
A smartphone based multi input workflow for non-invasive estimation of haemoglobin levels using machine learning techniques
This application expedites data collection and facilitates active learning of the model.
Reinforcement learning using Deep Q Networks and Q learning accurately localizes brain tumors on MRI with very small training sets
For comparison, we also trained and tested a keypoint detection supervised deep learning network for the same set of training / testing images.
A Data-Efficient Deep Learning Based Smartphone Application For Detection Of Pulmonary Diseases Using Chest X-rays
This paper introduces a paradigm of smartphone application based disease diagnostics that may completely revolutionise the way healthcare services are being provided.
Deep reinforcement learning to detect brain lesions on MRI: a proof-of-concept application of reinforcement learning to medical images
Reinforcement learning predicted testing set lesion locations with 85% accuracy, compared to roughly 7% accuracy for the supervised deep network.
