A Forward Learning Algorithm for Neural Memory Ordinary Differential Equations.

The deep neural network, based on the backpropagation learning algorithm, has achieved tremendous success. However, the backpropagation algorithm is consistently considered biologically implausible. Many efforts have recently been made to address these biological implausibility issues, nevertheless, these methods are tailored to discrete neural network structures.

ICNoduleNet: Enhancing Pulmonary Nodule Detection Performance on Sharp Kernel CT Imaging.

Thoracic computed tomography (CT) currently plays the primary role in pulmonary nodule detection, where the reconstruction kernel significantly impacts performance in computer-aided pulmonary nodule detectors. The issue of kernel selection affecting performance has been overlooked in pulmonary nodule detection. This paper first introduces a novel pulmonary nodule detection dataset named Reconstruction Kernel Imaging for Pulmonary Nodule Detection (RKPN) for quantifying algorithm differences between the two imaging types.

Deep learning for precise diagnosis and subtype triage of drug-resistant tuberculosis on chest computed tomography.

Deep learning, transforming input data into target prediction through intricate network structures, has inspired novel exploration in automated diagnosis based on medical images. The distinct morphological characteristics of chest abnormalities between drug-resistant tuberculosis (DR-TB) and drug-sensitive tuberculosis (DS-TB) on chest computed tomography (CT) are of potential value in differential diagnosis, which is challenging in the clinic. Hence, based on 1176 chest CT volumes from the equal number of patients with tuberculosis (TB), we presented a Deep learning-based system for TB drug resistance identification and subtype classification (DeepTB), which could automatically diagnose DR-TB and classify crucial subtypes, including rifampicin-resistant tuberculosis, multidrug-resistant tuberculosis, and extensively drug-resistant tuberculosis.

The Gap in the Thickness: Estimating Effectiveness of Pulmonary Nodule Detection in Thick- and Thin-Section CT Images with 3D Deep Neural Networks.

Background And Objectives: There is a noticeable gap in diagnostic evidence strength between the thick and thin scans of Low-Dose CT (LDCT) for pulmonary nodule detection. When the thin scans are needed is unknown, especially when aided with an artificial intelligence nodule detection system.

Methods: A case study is conducted with a set of 1,000 pulmonary nodule screening LDCT scans with both thick (5.

Deep Learning Empowers Lung Cancer Screening Based on Mobile Low-Dose Computed Tomography in Resource-Constrained Sites.

Background: Existing challenges of lung cancer screening included non-accessibility of computed tomography (CT) scanners and inter-reader variability, especially in resource-limited areas. The combination of mobile CT and deep learning technique has inspired innovations in the routine clinical practice.

Methods: This study recruited participants prospectively in two rural sites of western China.

DeepLN: A Multi-Task AI Tool to Predict the Imaging Characteristics, Malignancy and Pathological Subtypes in CT-Detected Pulmonary Nodules.

Objectives: Distinction of malignant pulmonary nodules from the benign ones based on computed tomography (CT) images can be time-consuming but significant in routine clinical management. The advent of artificial intelligence (AI) has provided an opportunity to improve the accuracy of cancer risk prediction.

Methods: A total of 8950 detected pulmonary nodules with complete pathological results were retrospectively enrolled.

Sandwich capture ultrasensitive sensor based on biohybrid interface for the detection of Cronobacter sakazakii.

A simple, rapid and ultrasensitive visual sensing method for the detection of Cronobacter sakazakii (C. sakazakii) based on a biohybrid interface was established. During the entire sensing process, quadruple-cascade amplification showed its superior sensing performance.

Enhancement of Damping-Like Field and Field-Free Switching in Pt/(Co/Pt)/PtMn Trilayer Films Prepared in the Presence of an Magnetic Field.

The current-induced magnetization switching and damping-like field in Pt/(Co/Pt)/PtMn trilayer films prepared with and without an in-plane field of 600 Oe have been studied systematically. In the presence of the field, a small in-plane bias field () is observed for films with PtMn thickness ≥5 nm, while there is no observable for PtMn thickness ≤3 nm. Nevertheless, a field-free switching of perpendicular magnetization of Co/Pt is observed for all the films with the PtMn thickness of 1-7 nm.

Rapid label-free colorimetric dual-functional aptasensor for β-lactoglobulin detection based on a rational tailoring strategy.

β-lactoglobulin (β-LG) is a nonnegligible allergenic protein found in cow milk. A label-free, enzyme-free, dual-functional aptameric sensor was constructed based on rational aptamer tailoring for β-LG detection. Via an established three-step rational tailoring strategy, the original aptamer was transformed from an inflexible antiparallel topology into the flexible, antiparallel/parallel hybrid topology, enlg2-pl3.

Deep Learning to Predict EGFR Mutation and PD-L1 Expression Status in Non-Small-Cell Lung Cancer on Computed Tomography Images.

Objective: The detection of epidermal growth factor receptor (EGFR) mutation and programmed death ligand-1 (PD-L1) expression status is crucial to determine the treatment strategies for patients with non-small-cell lung cancer (NSCLC). Recently, the rapid development of radiomics including but not limited to deep learning techniques has indicated the potential role of medical images in the diagnosis and treatment of diseases.

Methods: Eligible patients diagnosed/treated at the West China Hospital of Sichuan University from January 2013 to April 2019 were identified retrospectively.

RPLS-Net: pulmonary lobe segmentation based on 3D fully convolutional networks and multi-task learning.

Purpose: The robust and automatic segmentation of the pulmonary lobe is vital to surgical planning and regional image analysis of pulmonary related diseases in real-time Computer Aided Diagnosis systems. While a number of studies have examined this issue, the segmentation of unclear borders of the five lobes of the lung remains challenging because of incomplete fissures, the diversity of anatomical pulmonary information, and obstructive lesions caused by pulmonary diseases. This study proposes a model called Regularized Pulmonary Lobe Segmentation Network to accurately predict the lobes as well as the borders.

SurvNet: A Novel Deep Neural Network for Lung Cancer Survival Analysis With Missing Values.

Survival analysis is important for guiding further treatment and improving lung cancer prognosis. It is a challenging task because of the poor distinguishability of features and the missing values in practice. A novel multi-task based neural network, SurvNet, is proposed in this paper.

Automatic airway tree segmentation based on multi-scale context information.

Purpose: Airway tree segmentation plays a pivotal role in chest computed tomography (CT) analysis tasks such as lesion localization, surgical planning, and intra-operative guidance. The remaining challenge is to identify small bronchi correctly, which facilitates further segmentation of the pulmonary anatomies.

Methods: A three-dimensional (3D) multi-scale feature aggregation network (MFA-Net) is proposed against the scale difference of substructures in airway tree segmentation.

DeepLN: an artificial intelligence-based automated system for lung cancer screening.

Background: Lung cancer causes more deaths worldwide than any other cancer. For early-stage patients, low-dose computed tomography (LDCT) of the chest is considered to be an effective screening measure for reducing the risk of mortality. The accuracy and efficiency of cancer screening would be enhanced by an intelligent and automated system that meets or surpasses the diagnostic capabilities of human experts.

The application of artificial intelligence and radiomics in lung cancer.

Lung cancer is one of the most leading causes of death throughout the world, and there is an urgent requirement for the precision medical management of it. Artificial intelligence (AI) consisting of numerous advanced techniques has been widely applied in the field of medical care. Meanwhile, radiomics based on traditional machine learning also does a great job in mining information through medical images.

MSCS-DeepLN: Evaluating lung nodule malignancy using multi-scale cost-sensitive neural networks.

The accurate identification of malignant lung nodules using computed tomography (CT) screening images is vital for the early detection of lung cancer. It also offers patients the best chance of cure, because non-invasive CT imaging has the ability to capture intra-tumoral heterogeneity. Deep learning methods have obtained promising results for the malignancy identification problem; however, two substantial challenges still remain.

MediMLP: Using Grad-CAM to Extract Crucial Variables for Lung Cancer Postoperative Complication Prediction.

Lung cancer postoperative complication prediction (PCP) is significant for decreasing the perioperative mortality rate after lung cancer surgery. In this paper we concentrate on two PCP tasks: (1) the binary classification for predicting whether a patient will have postoperative complications; and (2) the three-class multi-label classification for predicting which postoperative complication a patient will experience. Furthermore, an important clinical requirement of PCP is the extraction of crucial variables from electronic medical records.

DeepLNAnno: a Web-Based Lung Nodules Annotating System for CT Images.

Lung cancer is one of the most common and fatal types of cancer, and pulmonary nodule detection plays a crucial role in the screening and diagnosis of this disease. A well-trained deep neural network model can help doctors to find nodules on computed tomography(CT) images while requiring lots of labeled data. However, currently available annotating systems are not suitable for annotating pulmonary nodules in CT images.

A long-term bench-scale investigation of permanganate consumption by aquifer materials.

In situ chemical oxidation (ISCO) applications using permanganate involve the injection or release of permanganate into the subsurface to destroy various target contaminants. Naturally occurring reduced components associated with aquifer materials can exert a significant oxidant demand thereby reducing the amount of permanganate available for the destruction of contaminants as well as reducing the overall rate of oxidation. Quantification of this natural oxidant demand (NOD) is a requirement for site-specific assessment and the design of cost-effective oxidant delivery systems.

An evaluation of the green chelant EDDS to enhance the stability of hydrogen peroxide in the presence of aquifer solids.

Hydrogen peroxide is a widely used in situ chemical oxidation reagent which relies on catalysts to generate the suite of reactive species that are required to aggressively remediate contaminated soils and groundwater. In the subsurface environment these catalysts are usually transition metals that are added to the injected solution, or are naturally occurring. Chelating agents are widely used to maintain an adequate dissolved transition metal concentration in near-neutral pH conditions; however, they can also be used to improve the persistence of H(2)O(2) in situations when the aquifer solids have sufficient transition metal content.