Medecine

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

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

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

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

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

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

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

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 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: 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

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

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

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

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

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

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

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

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

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 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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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