High throughput molecular imaging via deep learning enabled Raman spectroscopy

Raman spectroscopy enables non-destructive, label-free imaging with unprecedented molecular contrast but is limited by slow data acquisition . Here, we present a comprehensive framework for higher-throughput molecular imaging via deep learning enabled Raman imaging, termed DeepeR, trained on a large dataset of hyperspectral Raman images, with over 1.5 million spectra (400 hours of acquisition) in total .… Read the rest

Style invariant Cardiac Image Segmentation with Test time Augmentation

Deep models often suffer from severe performance drop due to appearance shift in the real clinical setting . Most of the existing learning-based methods rely on images from multiple sites/vendors or even corresponding labels… However, collecting enough unknown data to robustly model segmentation cannot always hold since the complex appearance shift caused by imaging factors in daily application .… Read the rest

Improving Automated COVID 19 Grading with Convolutional Neural Networks in Computed Tomography Scans An Ablation Study

Several studies have shown that COVID-19 classification and grading using computed tomography (CT) images can be automated with convolutional neural networks (CNNs) Many studies focused on reporting initial results of algorithms that were assembled from commonly used components… The choice of these components was often pragmatic rather than systematic .… Read the rest

Steering a Historical Disease Forecasting Model Under a Pandemic Case of Flu and COVID 19

Forecasting influenza in a timely manner aids health organizations and policymakers in adequate preparation and decision making . But effective influenza forecasting still remains a challenge despite increasing research interest… It is even more challenging amidst the COVID pandemic, when the influenza-like illness (ILI) counts is affected by various factors such as symptomatic similarities with COVID-19 and shift in healthcare seeking patterns of the general population .… Read the rest

Anonymization of labeled TOF MRA images for brain vessel segmentation using generative adversarial networks

Anonymization and data sharing are crucial for privacy protection and acquisition of large datasets for medical image analysis . Here, the brain’s unique structure allows for re-identification and thus requires non-conventional anonymization . Generative adversarial networks (GANs) have the potential to provide anonymous images while preserving predictive properties .… Read the rest

COVID MobileXpert On Device COVID 19 Patient Triage and Follow up using Chest X rays

COVID-MobileXpert: a lightweight deep neural network (DNN) based mobile app that can use chest X-ray (CXR) for case screening and radiological trajectory prediction… We design and implement a novel three-player knowledge transfer and distillation (KTD) framework including a pre-trained attending physician (AP) network that extracts CXR imaging features from a large scale of lung disease .… Read the rest

Evaluating Knowledge Transfer In Neural Network for Medical Images

Deep learning and knowledge transfer techniques have permeated the field of medical imaging and are considered as key approaches for revolutionizing diagnostic imaging practices . However, there are still challenges for the successful integration of deep learning into medical imaging tasks due to a lack of large annotated imaging data… To address this issue, we propose a teacher-student learning framework to transfer knowledge from a carefully pre-trained convolutional neural network (CNN) teacher to a student CNN .… Read the rest

Effective Transfer Learning for Identifying Similar Questions Matching User Questions to COVID 19 FAQs

People increasingly search online for answers to their medical questions but the rate at which medical questions are asked online significantly exceeds the capacity of qualified people to answer them . Many of these questions are not unique, and reliable identification of similar questions would enable more efficient and effective question answering Schema .… Read the rest

Contact Area Detector using Cross View Projection Consistency for COVID 19 Projects

The ability to determine what parts of objects and surfaces people touch as they go about their daily lives would be useful in understanding how the COVID-19 virus spreads . To determine whether a person has touched an object or surface using visual data, images, or videos, is a hard problem… Computer vision 3D reconstruction approaches project objects and the human body from the 2D image domain to 3D and perform 3D space intersection directly .… Read the rest

Transfer Learning for Protein Structure Classification and Function Inference at Low Resolution

Structure determination is key to understanding protein function at a molecular level . Researchers must still rely on expensive, time-consuming analytical methods to visualise detailed protein conformation . In this study, we demonstrate that it is possible to make accurate predictions of protein fold taxonomy from structures determined at low ($>$3 Angstroms) resolution, using a deep convolutional neural network trained on high-resolution structures .… Read the rest

MultiCheXNet A Multi Task Learning Deep Network For Pneumonia like Diseases Diagnosis From X ray Scans

MultiCheXNet is an end-to-end Multi-task learning model that is able to take advantage of different X-rays data sets of Pneumonia-like diseases in one neural architecture . The architecture can be trained on joint or dis-joint labeled data sets . The common encoder in our architecture can capture useful common features present in the different tasks .… Read the rest

An Uncertainty aware Transfer Learning based Framework for Covid 19 Diagnosis

The early and reliable detection of COVID-19 infected patients is essential to prevent and limit its outbreak . The PCR tests for the disease are not available in many countries and there are genuine concerns about their reliability and performance… Motivated by these shortcomings, this paper proposes a deep uncertainty-aware transfer learning framework for COVI-19 detection using medical images .… Read the rest

CovidCare Transferring Knowledge from Existing EMR to Emerging Epidemic for Interpretable Prognosis

The intelligent prognosis is in an urgent need to assist physicians to take an early intervention, prevent the adverse outcome, and optimize the medical resource allocation . In the early stage of the epidemic outbreak, the data available for analysis is limited due to the lack of effective diagnostic mechanisms, rarity of the cases, and privacy concerns .… Read the rest

Advances in Deep Learning for Hyperspectral Image Analysis Addressing Challenges Arising in Practical Imaging Scenarios

Deep neural networks have proven to be very effective for computer vision tasks, such as image classification, object detection, and semantic segmentation . In recent years, there has been an emergence of deep learning algorithms being applied to hyperspectral and multispectral imagery for remote sensing and biomedicine tasks .… Read the rest

Patient Specific Finetuning of Deep Learning Models for Adaptive Radiotherapy in Prostate CT

Contouring of the target volume and Organs-At-Risk (OARs) is a crucial step in radiotherapy treatment planning . In an adaptive radiotherapy setting, updated contours need to be generated based on daily imaging… In this work, we leverage personalized anatomical knowledge accumulated over the treatment sessions, to improve the segmentation accuracy of a pre-trained Convolution Neural Network (CNN) for a specific patient .… Read the rest

Deep COVID Predicting COVID 19 From Chest X Ray Images Using Deep Transfer Learning

The COVID-19 pandemic is causing a major outbreak in more than 150 countries around the world . Detecting this disease from radiography and radiology images is perhaps one of the fastest way to diagnose the patients . We evaluated these models on the remaining 3,000 images, and most of these networks achieved a sensitivity rate of 97\% ($\pm$ 5\%), while having a specificity rate of around 90\% .… Read the rest

Automated diagnosis of COVID 19 with limited posteroanterior chest X ray images using fine tuned deep neural networks

The novel coronavirus 2019 (COVID-19) is a respiratory syndrome that resembles pneumonia . The current diagnostic procedure of COVID-20 follows reverse-transcriptase polymerase polymerases polymerase chain reaction (RT-PCR) based approach . This article presents the random oversampling and weighted class loss function approach for unbiased fine-tuned learning (transfer learning) in various state-of-the-art deep learning approaches .… Read the rest

Study and development of a Computer Aided Diagnosis system for classification of chest x ray images using convolutional neural networks pre trained for ImageNet and data augmentation

Using Convolutional neural networks (ConvNets) are the actual standard for image recognizement and classification . The study uses ConvNets models available on the PyTorch platform: AlexNet, SqueezeNet, ResNet and Inception . We initially use three training styles: complete from scratch using random initialization, using a pre-trained ImageNet model training only the last layer adapted to our problem .… Read the rest

Classification Algorithm of Speech Data of Parkinsons Disease Based on Convolution Sparse Kernel Transfer Learning with Optimal Kernel and Parallel Sample Feature Selection

A novel PD classification algorithm based on sparse kernel transfer learning combined with a parallel optimization of samples and features is proposed . Sparse transfer learning is used to extract effective structural information of PD speech features from public datasets as source domain data, and the fast ADDM iteration is improved to enhance the information extraction performance .… Read the rest

Automatic classification between COVID 19 pneumonia non COVID 19 pneumonia and the healthy on chest X ray image combination of data augmentation methods

Study aimed to develop and validate computer-aided diagnosis (CXDx) system for classification between pneumonia, non-COVID-19 pneumonia, and the healthy on chest X-ray images . The proposed CADx system utilized VGG16 as a pre-trained model and combination of conventional method and mixup as data augmentation methods .… Read the rest

Detection of Coronavirus COVID 19 Associated Pneumonia based on Generative Adversarial Networks and a Fine Tuned Deep Transfer Learning Model using Chest X ray Dataset

Pneumonia is an infection that inflames the lungs’ air sacs of a human . Using a combination of GAN and deep transfer models proved it is efficiency according to testing accuracy measurement . AlexNet, GoogLeNet, Squeeznet, and Resnet18 are selected as deep transfer learning models to detect the pneumonia from chest x-rays .… Read the rest

Evaluating the Transferability and Adversarial Discrimination of Convolutional Neural Networks for Threat Object Detection and Classification within X Ray Security Imagery

X-ray imagery security screening is essential to maintaining transport security against a varying profile of threat or prohibited items . Particular interest lies in the automatic detection and classification of weapons such as firearms and knives within complex and cluttered images… Here, we address this problem by exploring various end-to-end object detection Convolutional Neural Network (CNN) architectures .… Read the rest

Knee menisci segmentation and relaxometry of 3D ultrashort echo time UTE cones MR imaging using attention U Net with transfer learning

The proposed deep learning-based approach can be used to efficiently generate automatic segmentations and determine meniscal relaxations times . The method has the potential to help radiologists with the assessment of meniscal diseases, such as osteoarthritis, like OA . The deep learning models achieved segmentation performance equivalent to the inter-observer variability of two radiologists.… Read the rest

Mammography Dual View Mass Correspondence

Standard breast cancer screening involves the acquisition of two mammography X-ray projections for each breast . We introduce a deep learning, patch-based Siamese network for lesion matching in dual-view mammography . Our locally-fitted approach generates a joint patch pair representation and comparison with a shared configuration between the two views .… Read the rest

Does Non COVID19 Lung Lesion Help Investigating Transferability in COVID 19 CT Image Segmentation

Deep learning has been adopted as an effective technique to aid COVID-19 detection and segmentation from computed tomography (CT) images… The major challenge lies in the inadequate public public COVI-19 datasets . The results reveal the benefits of transferring knowledge from non-COVID19 lung lesions, and learning from multiple lung lesion datasets can extract more general features, leading to accurate and robust pre-trained models .… Read the rest