## MoCo Pretraining Improves Representations and Transferability of Chest X ray Models

Momentum Contrast (MoCo) can leverage unlabeled data to produce pretrained models for subsequent fine-tuning on labeled data . While MoCo has demonstrated promising results on natural image classification tasks, its application to medical imaging tasks like chest X-ray interpretation has been limited .… Read the rest

## 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

## Attention with Multiple Sources Knowledges for COVID 19 from CT Images

Until now, Coronavirus SARS-CoV-2 has caused more than 850,000 deaths and infected more than 27 million individuals in over 120 countries . Besides principal polymerase chain reaction (PCR) tests, automatically identifying positive samples based on computed tomography (CT) scans can present a promising option in the early diagnosis of COVID-19 .… Read the rest

## Classification of COVID 19 in CT Scans using Multi Source Transfer Learning

The novel coronavirus disease COVID-19 has spread around the world infecting millions of people and upending the global economy . At times the turnaround results span as long as a couple of days, only to yield a roughly 70% sensitivity rate .… 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

## Face Mask Detection using Transfer Learning of InceptionV3

The world is facing a huge health crisis due to the rapid transmission of coronavirus (COVID-19) Several guidelines were issued by the World Health Organization (WHO) for protection against the spread . The proposed model is built by fine-tuning the pre-trained state-of-the-art deep learning model, InceptionV3 .… 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

## Advance Warning Methodologies for COVID 19 using Chest X Ray Images

Coronavirus disease 2019 (COVID-19) has rapidly become a global health concern after its first known detection in December 2019 . As a result, accurate and reliable advance warning system for the early diagnosis has now become a priority . Both compact classifiers and deep learning approaches are considered in this study .… 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

## Multimodal Inductive Transfer Learning for Detection of Alzheimer s Dementia and its Severity

Alzheimer’s disease is estimated to affect around 50 million people worldwide and is rising rapidly, with a global economic burden of nearly a trillion dollars . We present a novel architecture that leverages acoustic, cognitive, and linguistic features to form a multimodal ensemble system .… 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

## Leveraging Medical Visual Question Answering with Supporting Facts

IBM Research AI (Almaden) team’s participation in the ImageCLEF 2019 VQA-Med competition . The challenge consists of four question-answering tasks based on radiology images . The diversity of imaging modalities, organs and disease types combined with a small imbalanced training set made this a highly complex problem .… 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

## COVID 19 therapy target discovery with context aware literature mining

The abundance of literature related to the widespread COVID-19 pandemic is beyond manual inspection of a single expert . We propose a novel embedding generation technique that leverages SciBERT language model pretrained on a large multi-domain corpus of scientific publications and fine-tuned for domain adaptation on the CORD-19 dataset .… Read the rest

## An Explainable Machine Learning Model for Early Detection of Parkinson s Disease using LIME on DaTscan Imagery

Parkinson’s disease (PD) is a degenerative and progressive neurological condition . Early diagnosis can improve treatment for patients and is performed through dopaminergic imaging techniques like the SPECT DaTscan . The proposed system may effectively aid medical workers in the early diagnosis of Parkinson’s Disease .… 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

## Depressive Drug Abusive or Informative Knowledge aware Study of News Exposure during COVID 19 Outbreak

The COVID-19 pandemic is having a serious adverse impact on the lives of people across the world . It has exacerbated community-wide depression, and led to increased drug abuse brought about by isolation of individuals as a result of lockdown .… Read the rest

## Reliable Tuberculosis Detection using Chest X ray with Deep Learning Segmentation and Visualization

Tuberculosis (TB) is a chronic lung disease that occurs due to bacterial infection and is one of the top 10 leading causes of death . The proposed method with state-of-the-art performance can be useful in the computer-aided faster diagnosis of tuberculosis .… 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

## A Comparative Study on Early Detection of COVID 19 from Chest X Ray Images

Coronavirus disease 2019 (COVID-19) has rapidly become a global health concern after its first known detection in December 2019 . As a result, accurate and reliable advance warning system for the early diagnosis has now become a priority . Both compact classifiers and deep learning approaches are considered in this study .… 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

## Multi Channel Transfer Learning of Chest X ray Images for Screening of COVID 19

The 2019 novel coronavirus (COVID-19) has spread rapidly all over the world and it is affecting the whole society . The current gold standard test for screening patients is the polymerase chain reaction test… However, the COV-19 test kits are not widely available and time-consuming .… 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

## Predictive modeling of brain tumor A Deep learning approach

Recent advancements in the field of deep learning have made it possible to detect the growth of cancerous tissue just by a patient’s brain Magnetic Resonance Imaging (MRI) scans . Resnet-50 model achieves the highest accuracy and least false negative rates as 95% and zero respectively .… 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

## A Novel GAN with Deep Transfer Learning for Corona virus Detection in Chest X ray Images

A GAN with deep transfer learning for coronavirus detection in chest x-ray images is presented . The lack of benchmark datasets for COVID-19 is the main motivation of this research . The GAN helps in generating more images from the original dataset to be 30 times larger than the originally collected dataset .… Read the rest

## Comparison of Deep Learning Approaches for Multi Label Chest X Ray Classification

The increased availability of X-ray image archives has triggered a growing interest in deep learning techniques . We investigate a powerful network architecture in detail: the ResNet-50… Building on prior work in this domain, we consider transfer learning with and without fine-tuning as well as the training of a dedicated X-rays network from scratch .… Read the rest

## 3D Convolutional Encoder Decoder Network for Low Dose CT via Transfer Learning from a 2D Trained Network

Low-dose computed tomography (CT) has attracted a major attention in the medical imaging field, since CT-associated x-ray radiation carries health risks for patients . The reduction of CT radiation dose compromises the signal-to-noise ratio, and may compromise the image quality and the diagnostic performance .… Read the rest

## Relational Modeling for Robust and Efficient Pulmonary Lobe Segmentation in CT Scans

Recent works based on convolution neural networks have achieved good performance for this task… However, they are still limited in capturing structured relationships due to the nature of convolution . Pulmonary lobe segmentation in computed tomography scans is essential for regional assessment of pulmonary diseases .… 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

## Deep Learning for Apple Diseases Classification and Identification

This paper is an attempt to provide the timely and accurate detection and identification of apple diseases . The identification of various apple diseases is challenging for the farmers as the symptoms produced by different diseases may be very similar, and may be present simultaneously .… Read the rest

## Semantic Aware Generative Adversarial Nets for Unsupervised Domain Adaptation in Chest X ray Segmentation

Deep neural networks often suffer from degraded performance when applied to new test datasets with domain shift . In this way, the segmentation DNN learned from the source domain is able to be directly generalized to the transformed test image .… 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

## Diagnosing COVID 19 Pneumonia from X Ray and CT Images using Deep Learning and Transfer Learning Algorithms

COVID-19 (also known as 2019 Novel Coronavirus) first emerged in Wuhan, China and spread across the globe with unprecedented effect and has now become the greatest crisis of the modern era . The lack of a publicly available dataset of X-ray and CT images makes the design of such AI tools a challenging task .… 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

## Identifying disease free chest X ray images with deep transfer learning

Chest X-rays are among the most commonly used medical image modalities . The proposed solution has the potential to cut in half the number of disease-free CXRs examined by radiologists . The probability threshold for classification is optimized for 100% precision for the normal class, ensuring no sick patients are released .… Read the rest

## Implementation of Deep Neural Networks to Classify EEG Signals using Gramian Angular Summation Field for Epilepsy Diagnosis

This paper evaluates the approach of imaging timeseries data such as EEG in the diagnosis of epilepsy through Deep Neural Network (DNN) Three pre-trained DNN such as the AlexNet, VGG16, and VGG19 are validated for epilepsy detection based on the transfer learning approach .… 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

## MS Net Multi Site Network for Improving Prostate Segmentation with Heterogeneous MRI Data

Automated prostate segmentation in MRI is highly demanded for computer-assisted diagnosis . The prostate MRIs from different sites present heterogeneity due to the differences in scanners and imaging protocols, raising challenges for effective ways of aggregating multi-site data for network training .… Read the rest

## A Qualitative Evaluation of Language Models on Automatic Question Answering for COVID 19

COVID-19 has resulted in an ongoing pandemic and as of 12 June 2020, has caused more than 7.4 million cases and over 418,000 deaths . Online communities, forums, and social media provide potential venues to search for relevant questions and answers, or post questions and seek answers from other members .… 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