False Positive Reduction Research Articles

Le recours aux dossiers des patients est-il nécessaire pour l’évaluation du risque de récidive sexuelle, violente et générale ?

IntroductionSexual, violent and general recidivism risk scales are widely used in a number of countries. Their psychometric qualities are generally considered to be good. However, in practice they may vary in the quality of prediction of risk of sexual, violent and general recidivism, in particular because of the sources of the information collected. In France, the medical records of incarcerated patients are kept by health-care professionals. Although regulated, the content and quality of these records vary widely from one patient to another. The criminal justice system holds the criminal records of convicted and imprisoned persons. There is no set list of documents contained in these records. For caregivers and researchers, access to criminal records is difficult because of the confidentiality to which legal professionals are subject. The aim of our study was to investigate whether using medical files in addition to structured interviews can improve the assessment and management of the risks of sexual, violent and general recidivism. Material and MethodA total of 128 perpetrators of violence were assessed using three scales of risk of sexual, violent and general recidivism. Scores for the items of the scales were compared between (a) those that were based on medical records and an interview, and (b) those based only on an interview. Results and discussionFirst, differences in scores between the two groups (assessed through interview only, and assessed through interview and use of medical records) were observed on the RSVP, HCR-20 and LS/CMI scales. Secondly, most of the results indicate that the overall level of risk was perceived as lower when medical records were used, which would, indirectly, lead to a reduction in false positives when evaluating perpetrators of sexual violence. Thirdly, the point-by-point analysis shows that the use of information contained in the medical records reduces the weight of present and future factors (e.g. the physical and psychological stress of recent events), increases the weight of past factors (e.g. history of sexual violence), and can increase the weight of certain factors that can lead to more negative emotions in the assessor (e.g. deviance). These results can be explained by (i) the emotional functioning of the persons assessed (particularly defensive processes or memory difficulties), (ii) the attitude of the aggressor (particularly the presence of emotional and cognitive biases), (iii) the nature of the information (particularly “hot” cognitions or those leading to greater social desirability). The limitations of the study concern the relatively small number of participants, the environment in which the file was transmitted and the very heterogeneous and sometimes relatively incomplete composition of the files. ConclusionThe use of information contained in medical files impacts the results of recidivism risk scales and restores a balance to the factors. In France, risk scales are currently being introduced, although their use is still limited in the health field. However, ethical use of these scales raises the issue of the homogenisation of the content of medical records and access to criminal records in order to enable future research to confirm whether the use of information provided in medical and criminal records can improve the quality of assessment and treatment of offenders.

A Deep Learning Based Brain Metastases Segmentation Web Platform for Stereotactic Radiosurgery

Stereotactic radiosurgery (SRS) enables the treatment of multiple (> 4) brain metastases (mBMs) patients with reduced neurotoxicity. BMs delineation serves as an essential part in the SRS management involving treatment planning and follow-up. However, manual contouring is highly time-consuming, especially in patients with mBMs. In this study, we propose a deep learning-based BMs segmentation web platform including BMs labeling and database management to improve the efficiency of SRS workflow.The platform is designed with a web client and a back-end server. The web client enables user interactions and data visualization, while the back-end server embeds the core algorithms and maintains a database. All the embedded deep learning models were previously-trained using 271 T1c MRI data with 1058 BMs in total. Once the user imports new MRI data from the client, the server will execute tasks including: 1) CNN-based BMs auto-segmentation 2) Siamese network based false-positive segmentation reduction 3) Affine registration-based BMs labeling, and then send the results back to the web client. The web client displays the received segmentation results, and allows the users to conduct further modifications and export as DICOM RTSTRUCT files. In addition, the server will update the database with the new data. Users can access this database from the web client to retrieve any patient data including image, segmentation and other metadata.The entire process takes about 4 to 5 minutes to complete per new data. The platform can segment mBMs with initial averaged sensitivity of 0.96 and specificity of 0.59. Then after the false-positive reduction, the averaged sensitivity and specificity currently are 0.86 and 0.89. Due to the flexibility of false-positive reduction algorithm, we can balance the sensitivity/specificity as clinical need. The segmentation accuracy is evaluated by the mean (std) Hausdorff distance as 2.75 (0.57) mm, the mean (std) center of mass shift as 1.41 (0.32) mm, and the mean (std) of surface-to-surface distance as 1.01 (0.28) mm, with the BMs labeling accuracy as 100%.The deep learning BMs segmentation web platform can accurately segment and label mBMs, and maintain a database for improved data management. It can facilitate the efficiency of the SRS workflow including planning and treatment follow-up, and can be a promising tool for the BMs management.

Automatic quantification of white matter hyperintensities on T2-weighted fluid attenuated inversion recovery magnetic resonance imaging

White matter hyperintensities (WMH) are areas of increased signal visualized on T2-weighted fluid attenuated inversion recovery (FLAIR) brain magnetic resonance imaging (MRI) sequences. They are typically attributed to small vessel cerebrovascular disease in the context of aging. Among older adults, WMH are associated with risk of cognitive decline and dementia, stroke, and various other health outcomes. There has been increasing interest in incorporating quantitative WMH measurement as outcomes in clinical trials, observational research, and clinical settings. Here, we present a novel, fully automated, unsupervised detection algorithm for WMH segmentation and quantification. The algorithm uses a robust preprocessing pipeline, including brain extraction and a sample-specific mask that incorporates spatial information for automatic false positive reduction, and a half Gaussian mixture model (HGMM). The method was evaluated in 24 participants with varying degrees of WMH (4.9–78.6 cm3) from a community-based study of aging and dementia with dice coefficient, sensitivity, specificity, correlation, and bias relative to the ground truth manual segmentation approach performed by two expert raters. Results were compared with those derived from commonly used available WMH segmentation packages, including SPM lesion probability algorithm (LPA), SPM lesion growing algorithm (LGA), and Brain Intensity AbNormality Classification Algorithm (BIANCA). The HGMM algorithm derived WMH values that had a dice score of 0.87, sensitivity of 0.89, and specificity of 0.99 compared to ground truth. White matter hyperintensity volumes derived with HGMM were strongly correlated with ground truth values (r = 0.97, p = 3.9e-16), with no observable bias (−1.1 [−2.6, 0.44], p-value = 0.16). Our novel algorithm uniquely uses a robust preprocessing pipeline and a half-Gaussian mixture model to segment WMH with high agreement with ground truth for large scale studies of brain aging.

An efficient multi-path 3D convolutional neural network for false-positive reduction of pulmonary nodule detection.

Considering that false-positive and true pulmonary nodules are highly similar in shapes and sizes between lung computed tomography scans, we develop and evaluate a false-positive nodules reduction method applied to the computer-aided diagnosis system. To improve the pulmonary nodule diagnosis quality, a 3D convolutional neural networks (CNN) model is constructed to effectively extract spatial information of candidate nodule features through the hierarchical architecture. Furthermore, three paths corresponding to three receptive field sizes are adopted and concatenated in the network model, so that the feature information is fully extracted and fused to actively adapting to the changes in shapes, sizes, and contextual information between pulmonary nodules. In this way, the false-positive reduction is well implemented in pulmonary nodule detection. Multi-path 3D CNN is performed on LUNA16 dataset, which achieves an average competitive performance metric score of 0.881, and excellent sensitivity of 0.952 and 0.962 occurs to 4, 8 FP/Scans. By constructing a multi-path 3D CNN to fully extract candidate target features, it accurately identifies pulmonary nodules with different sizes, shapes, and background information. In addition, the proposed general framework is also suitable for similar 3D medical image classification tasks.

An Iterative Algorithm for Semisupervised Classification of Hotspots on Bone Scintigraphies of Patients with Prostate Cancer.

Prostate cancer (PCa) is the second most diagnosed cancer in men. Patients with PCa often develop metastases, with more than 80% of this metastases occurring in bone. The most common imaging technique used for screening, diagnosis and follow-up of disease evolution is bone scintigraphy, due to its high sensitivity and widespread availability at nuclear medicine facilities. To date, the assessment of bone scans relies solely on the interpretation of an expert physician who visually assesses the scan. Besides this being a time consuming task, it is also subjective, as there is no absolute criteria neither to identify bone metastases neither to quantify them by a straightforward and universally accepted procedure. In this paper, a new algorithm for the false positives reduction of automatically detected hotspots in bone scintigraphy images is proposed. The motivation relies in the difficulty of building a fully annotated database. In this way, our algorithm is a semisupervised method that works in an iterative way. The ultimate goal is to provide the physician with a fast, precise and reliable tool to quantify bone scans and evaluate disease progression and response to treatment. The algorithm is tested in a set of bone scans manually labeled according to the patient’s medical record. The achieved classification sensitivity, specificity and false negative rate were 63%, 58% and 37%, respectively. Comparison with other state-of-the-art classification algorithms shows superiority of the proposed method.

Tips for a reduction of false positives in manual RT-PCR diagnostics of SARS-CoV-2

RT-PCR is the standard gold technique for testing the presence of RNA of the coronavirus causing Severe Acute Respiratory Syndrome (SARS-CoV-2) due to its high specificity and sensitivity. Despite its general use and reliability, no lab in the world is immune to the generation of false positives. These errors cause a loss of confidence in the technique's power and damage the image of laboratories. More importantly, they can take a toll on tested individuals and have economic, psychological, and health-associated effects. Most false positives are caused during a manual operation inside the laboratory. However, not much has been published about the errors associated with particular laboratory techniques used to detect the virus since the beginning of the actual pandemic. This work precisely reflects on events that occur during manual RT-PCR diagnostics in a COVID-19 laboratory, providing tips for reducing false-positive results.

Automatic breast mass detection in mammograms using density of wavelet coefficients and a patch-based CNN.

For the purpose of accurate and efficient mass detection in full-field digital mammograms, weproposeamethodfor automated mass detection that consists oftwostages: suspicious region localization and false-positive (FP) reduction, by classifying these regions into mass and non-mass regions (normal tissues). In the first stage, the density of the wavelet coefficients based on Quincunx Lifting Scheme (DWC-QLS) is used to find suspicious regions (regions of interest, ROIs) in full mammograms. In the second stage, a patch-based CNN classifier is developed as an FP reduction to classify the suspicious regions. The main aim of this stage is to reduce the false-positive suspicious regions while keeping the true-positive suspicious regions. To further improve the performance of the FP reduction, the effectiveness of different transfer learning strategies is further explored and the best fine-tuning strategy in training InceptionV3 model is determined experimentally. The experimental results show that the proposed method can achieve an overall performance of 0.98 TPR @1.43 FPI on the INbreast database. In addition, the suggested segmentation method detects the mass location with 100% sensitivity and average of 5.4 false positives per image. Based on the obtained results,the introduced method was able to successfully detectand classify suspicious regions in digital mammograms and provide better TPR and FPI results in comparison with other state-of-the-art method.

Lung Cancer Detection using CT SCAN Images

Detection of pulmonary nodules has a crucial effect on the diagnosis of lung cancer, but the detection is a nontrivial task, not only because the appearance of pulmonary nodules varies in a wide range, but also because nodule densities have low contrast against adjacent vessel segments and other lung tissues. Computed tomography (CT) has been shown as the most popular imaging modality for nodule detection, because it has the ability to provide reliable image textures for the detection of small nodules. The development of lung nodule CADe systems using CT imaging modality has made good progress over the past decade. Generally, such CADe systems consist of three stages: 1) image pre-processing, 2) initial nodule candidates (INCs) identification, and 3) false positive (FP) reduction of the INCs with preservation of the true positives (TPs). In the pre-processing stage, the system aims to largely reduce the search space to the lungs, where a segmentation of the lungs from the entire chest volume is usually required. Because of the high image contrast between lung fields and the surrounding body tissue, image intensity-based simple thresholding is effective, and is currently the most commonly used technique for lung segmentation. This paper proposes an adaptive solution to mitigate the difficulty of thresholding-based method in lung segmentation. Sufficient detection power for nodule candidates is inevitably accompanied by many (obvious) FPs. A rule-based filtering operation is often employed to cheaply and drastically reduce the number of obvious FPs, so that their influence on the computationally more expensive learning process can be eliminated. In general, FP reduction using machine learning has been extensively studied in the literature. Compared with unsupervised learning that aims to find hidden structures in unlabelled data, supervised learning, which aims to infer a function from labelled training data, is more frequently used to design a CADe system. Compared with the existing approaches, the morphology based lung cancer detection can be an alternative with either comparable detection performance and less computational cost, or comparable cost and better detection performance.

Automatic Handgun Detection with Deep Learning in Video Surveillance Images

There is a great need to implement preventive mechanisms against shootings and terrorist acts in public spaces with a large influx of people. While surveillance cameras have become common, the need for monitoring 24/7 and real-time response requires automatic detection methods. This paper presents a study based on three convolutional neural network (CNN) models applied to the automatic detection of handguns in video surveillance images. It aims to investigate the reduction of false positives by including pose information associated with the way the handguns are held in the images belonging to the training dataset. The results highlighted the best average precision (96.36%) and recall (97.23%) obtained by RetinaNet fine-tuned with the unfrozen ResNet-50 backbone and the best precision (96.23%) and F1 score values (93.36%) obtained by YOLOv3 when it was trained on the dataset including pose information. This last architecture was the only one that showed a consistent improvement—around 2%—when pose information was expressly considered during training.

End-to-end prostate cancer detection in bpMRI via 3D CNNs: Effects of attention mechanisms, clinical priori and decoupled false positive reduction

We present a multi-stage 3D computer-aided detection and diagnosis (CAD) model22Source code and algorithm have been made publicly available at: https://github.com/DIAGNijmegen/prostateMR_3D-CAD-csPCa https://grand-challenge.org/algorithms/prostate-mri-cad-cspca for automated localization of clinically significant prostate cancer (csPCa) in bi-parametric MR imaging (bpMRI). Deep attention mechanisms drive its detection network, targeting salient structures and highly discriminative feature dimensions across multiple resolutions. Its goal is to accurately identify csPCa lesions from indolent cancer and the wide range of benign pathology that can afflict the prostate gland. Simultaneously, a decoupled residual classifier is used to achieve consistent false positive reduction, without sacrificing high sensitivity or computational efficiency. In order to guide model generalization with domain-specific clinical knowledge, a probabilistic anatomical prior is used to encode the spatial prevalence and zonal distinction of csPCa. Using a large dataset of 1950 prostate bpMRI paired with radiologically-estimated annotations, we hypothesize that such CNN-based models can be trained to detect biopsy-confirmed malignancies in an independent cohort.For 486 institutional testing scans, the 3D CAD system achieves 83.69±5.22% and 93.19±2.96% detection sensitivity at 0.50 and 1.46 false positive(s) per patient, respectively, with 0.882±0.030 AUROC in patient-based diagnosis –significantly outperforming four state-of-the-art baseline architectures (U-SEResNet, UNet++, nnU-Net, Attention U-Net) from recent literature. For 296 external biopsy-confirmed testing scans, the ensembled CAD system shares moderate agreement with a consensus of expert radiologists (76.69%; kappa = 0.51±0.04) and independent pathologists (81.08%; kappa = 0.56±0.06); demonstrating strong generalization to histologically-confirmed csPCa diagnosis.

WDO optimized detection for mammographic masses and its diagnosis: A unified CAD system

For decades, breast cancer is the leading cause of cancer-related deaths among women. Early detection and diagnosis of breast cancer can effectively reduce the mortality rate. Masses are one of the manifestations of imperceptible breast cancer visible in mammograms whose detection and diagnosis is a challenging task due to its subtle nature. In this paper, a unified computer-aided detection/diagnosis scheme is proposed for the automatic detection and diagnosis of mammographic masses. Being simple and effective, multilevel image thresholding based on Otsu’s method is considered for localization of suspicious mass lesions, but its performance is compromised while defining the precise threshold value especially when the intensity profile in and around the anomalies does not vary much. To mitigate the issue, a nature-inspired optimization algorithm, wind driven optimization, is proposed which in combination with Otsu’s multilevel thresholding identifies the potential candidates of mass lesions. To reduce the false positives and diagnose the malignant masses, a texture-based multi-gradient local quinary pattern (M-GQP) feature is introduced which renders higher consistency in computing gradient information from the uniform and near-uniform areas. Due to eight distinct Sobel masks, it offers a better resolution in edge regions in addition to the micro information at different orientations. Four popular classifiers (support vector machine, Fisher’s linear discriminant, K-nearest neighbor, and an ensemble classifier) are also investigated to discriminate malignant tumors from benign lesions and normal tissues. The proposed integrated approach for the detection and characterization of mammographic masses is evaluated on two benchmark databases — mini-MIAS and DDSM comprising of 68 and 500 mammograms, respectively and achieved a sensitivity of 96.9% and 96.2% with 0.09 and 0.17 false positives per image after false positive reduction for the respective datasets. The diagnosis of malignant masses prior to and following false-positive reduction observes an Az value of 0.98 and 0.94 with an accuracy of 99.04% and 92.65%, respectively for the mini-MIAS dataset while the same for the DDSM are 0.99 and 0.92 with an accuracy of 98.33% and 78.50%, respectively. In a three-class classification (normal, benign and malignant), an accuracy of 99.04% and 97.76% with an F1 score of 0.98 and 0.97 for malignancy is obtained for the mini-MIAS and DDSM images, respectively which are promising in nature when compared to other competing schemes in the state-of-the-art.

Cleaning the Medicago Microarray Database to Improve Gene Function Analysis.

Transcriptomics studies have been facilitated by the development of microarray and RNA-Seq technologies, with thousands of expression datasets available for many species. However, the quality of data can be highly variable, making the combined analysis of different datasets difficult and unreliable. Most of the microarray data for Medicago truncatula, the barrel medic, have been stored and made publicly accessible on the web database Medicago truncatula Gene Expression atlas (MtGEA). The aim of this work is to ameliorate the quality of the MtGEA database through a general method based on logical and statistical relationships among parameters and conditions. The initial 716 columns available in the dataset were reduced to 607 by evaluating the quality of data through the sum of the expression levels over the entire transcriptome probes and Pearson correlation among hybridizations. The reduced dataset shows great improvements in the consistency of the data, with a reduction in both false positives and false negatives resulting from Pearson correlation and GO enrichment analysis among genes. The approach we used is of general validity and our intent is to extend the analysis to other plant microarray databases.

Breast cancer diagnosis using thermal image analysis: A data-driven approach based on swarm intelligence and supervised learning for optimized feature selection

Breast cancer is one of the deadliest forms of cancer in women but the disease has a good prognosis when diagnosed early. The gold standard for the diagnosis of breast cancer is mammography imaging analysis but the acquisition of mammograms is a painful and embarrassing procedure for women involving breast compression. Alternative methods have been investigated in the last years, including breast thermography, which does not involve ionizing radiation, pain or contact with the breast. However, the accuracy of these modern techniques still needs to be improved to allow the widespread use in practical applications but machine learning techniques have brought in an increased accuracy and reduction in false positives and false negatives to the analysis of breast thermograms. We propose a methodology for detecting and classifying breast lesions using a database of real images of Brazilian patients. We divide our methodology into three steps. In the first step, the shape and texture characteristics of breast thermograms are extracted using Zernike and Haralick moments. The second step is the feature selection process using multi-objective binary optimization algorithms based on swarm intelligence. The third step is to analyze the best vectors’ classification using eleven algorithms such as Convolutional Neural Networks, Extreme Learning Machines, and Support Vector Machines. Finally, we discuss the computational time and performance of various techniques based on swarm intelligence, artificial neural networks, and statistical models to improve the computational time and accuracy of breast cancer diagnoses. Indeed, we observe that the feature selection process has helped us decrease computational time with a high potential to improve diagnostic accuracy. We also demonstrate that the extracted features considering the shape of breast lesions are highly important to a high diagnostic accuracy.

Automatic lung nodule detection in thoracic CT scans using dilated slice-wise convolutions.

Most state-of-the-art automated medical image analysis methods for volumetric data rely on adaptations of two-dimensional (2D) and three-dimensional (3D) convolutional neural networks (CNNs). In this paper, we develop a novel unified CNN-based model that combines the benefits of 2D and 3D networks for analyzing volumetric medical images. In our proposed framework, multiscale contextual information is first extracted from 2D slices inside a volume of interest (VOI). This is followed by dilated 1D convolutions across slices to aggregate in-plane features in a slice-wise manner and encode the information in the entire volume. Moreover, we formalize a curriculum learning strategy for a two-stage system (i.e., a system that consists of screening and false positive reduction), where the training samples are presented to the network in a meaningful order to further improve the performance. We evaluated the proposed approach by developing a computer-aided detection (CADe) system for lung nodules. Our results on 888 CT exams demonstrate that the proposed approach can effectively analyze volumetric data by achieving a sensitivity of > 0.99 in the screening stage and a sensitivity of > 0.96 at eight false positives per case in the false positive reduction stage. Our experimental results show that the proposed method provides competitive results compared to state-of-the-art 3D frameworks. In addition, we illustrate the benefits of curriculum learning strategies in two-stage systems that are of common use in medical imaging applications.

On the Design of IoT Security: Analysis of Software Vulnerabilities for Smart Grids

The 5G communication network will underpin a vast number of new and emerging services, paving the way for unprecedented performance and capabilities in mobile networks. In this setting, the Internet of Things (IoT) will proliferate, and IoT devices will be included in many 5G application contexts, including the Smart Grid. Even though 5G technology has been designed by taking security into account, design provisions may be undermined by software-rooted vulnerabilities in IoT devices that allow threat actors to compromise the devices, demote confidentiality, integrity and availability, and even pose risks for the operation of the power grid critical infrastructures. In this paper, we assess the current state of the vulnerabilities in IoT software utilized in smart grid applications from a source code point of view. To that end, we identified and analyzed open-source software that is used in the power grid and the IoT domain that varies in characteristics and functionality, ranging from operating systems to communication protocols, allowing us to obtain a more complete view of the vulnerability landscape. The results of this study can be used in the domain of software development, to enhance the security of produced software, as well as in the domain of automated software testing, targeting improvements to vulnerability detection mechanisms, especially with a focus on the reduction of false positives.

High precision localization of pulmonary nodules on chest CT utilizing axial slice number labels

BackgroundReidentification of prior nodules for temporal comparison is an important but time-consuming step in lung cancer screening. We develop and evaluate an automated nodule detector that utilizes the axial-slice number of nodules found in radiology reports to generate high precision nodule predictions.Methods888 CTs from Lung Nodule Analysis were used to train a 2-dimensional (2D) object detection neural network. A pipeline of 2D object detection, 3D unsupervised clustering, false positive reduction, and axial-slice numbers were used to generate nodule candidates. 47 CTs from the National Lung Cancer Screening Trial (NLST) were used for model evaluation.ResultsOur nodule detector achieved a precision of 0.962 at a recall of 0.573 on the NLST test set for any nodule. When adjusting for unintended nodule predictions, we achieved a precision of 0.931 at a recall 0.561, which corresponds to 0.06 false positives per CT. Error analysis revealed better detection of nodules with soft tissue attenuation compared to ground glass and undeterminable attenuation. Nodule margins, size, location, and patient demographics did not differ between correct and incorrect predictions.ConclusionsUtilization of axial-slice numbers from radiology reports allowed for development of a lung nodule detector with a low false positive rate compared to prior feature-engineering and machine learning approaches. This high precision nodule detector can reduce time spent on reidentification of prior nodules during lung cancer screening and can rapidly develop new institutional datasets to explore novel applications of computer vision in lung cancer imaging.

Automatic detection and segmentation of lung nodules in different locations from CT images based on adaptive α‐hull algorithm and DenseNet convolutional network

AbstractAutomatic lung nodules detection and segmentation can assist doctors in better diagnosis and treatment for lung cancer. However, precise detection and segmentation are still challenging, because lung nodules can have different contours or locations and may be attached to other tissues, such as neighboring blood vessel and pleural surface. In this study, an automatic detection and segmentation method for lung nodules in different locations has been developed. First, we apply Otsu thresholding to segment lung parenchyma. Next, a morphological opening operation is carried out to remove blood vessels. Then α‐hull operation is proposed to correct lung contours and optimal α values can be acquired adaptively. Finally, DenseNet convolutional network is applied to classify true lung nodules from all nodule candidates. We select the intersection area of at least three radiologists' annotations as ground truth and validate our method on 466 nodules including well‐circumscribed, juxta‐vascular, juxta‐pleural, and pleural tail. Our study not only concentrates on false positive reduction but also evaluates segmentation performance. To give a more comprehensive quantitative evaluation of nodule segmentation, we use evaluation metrics including Jaccard index (JI), dice similar coefficient (DSC), Hausdorff distance, under‐segmentation rate, over‐segmentation rate, sensitivity, specificity, accuracy, and false positive rate. Overall results are 0.6385 ± 0.1309, 0.7710 ± 0.1057, 3.5123 ± 3.1251, 0.1769 ± 0.1308, 0.1848 ± 0.1463, 0.7936 ± 0.1417, 0.9998 ± 0.0003, 0.9997 ± 0.0003, and 0.0002 ± 0.0003, respectively. These metrics can demonstrate segmentation performance in multiple dimensions. As a general and automatic detection and segmentation framework for lung nodules in different locations, this study achieves better performance than previous approaches in JI and DSC.

Detection of Lung Nodules on CT Images based on the Convolutional Neural Network with Attention Mechanism

The development of Computer-aided diagnosis (CAD) systems for automatic lung nodule detection through thoracic computed tomography (CT) scans has been an active area of research in recent years. Lung Nodule Analysis 2016 (LUNA16 challenge) encourages researchers to suggest a variety of successful nodule detection algorithms based on two key stages (1) candidates detection, (2) false-positive reduction. In the scope of this paper, a new convolutional neural network (CNN) architecture is proposed to efficiently solve the second challenge of LUNA16. Specifically, we find that typical CNN models pay little attention to the characteristics of input data, in order to address this constraint, we apply the attention-mechanism: propose a technique to attach Squeeze and Excitation-Block (SE-Block) after each convolution layer of CNN to emphasize important feature maps related to the characteristics of the input image - forming Attention sub-Convnet. The new CNN architecture is suggested by connecting the Attention sub-Convnets. In addition, we also analyze the selection of triplet loss or softmax loss functions to boost the rating performance of the proposed CNN. From the study, this is agreed to select softmax loss during the CNN training phase and triplet loss for the testing phase. Our suggested CNN is used to minimize the number of redundant candidates in order to improve the efficiency of false-positive reduction with the LUNA database. The results obtained in comparison to the previous models indicate the feasibility of the proposed model.

Attention-embedded complementary-stream CNN for false positive reduction in pulmonary nodule detection

False positive reduction plays a key role in computer-aided detection systems for pulmonary nodule detection in computed tomography (CT) scans. However, this remains a challenge owing to the heterogeneity and similarity of anisotropic pulmonary nodules. In this study, a novel attention-embedded complementary-stream convolutional neural network (AECS-CNN) is proposed to obtain more representative features of nodules for false positive reduction. The proposed network comprises three function blocks: 1) attention-guided multi-scale feature extraction, 2) complementary-stream block with an attention module for feature integration, and 3) classification block. The inputs of the network are multi-scale 3D CT volumes due to variations in nodule sizes. Subsequently, a gradual multi-scale feature extraction block with an attention module was applied to acquire more contextual information regarding the nodules. A subsequent complementary-stream integration block with an attention module was utilized to learn the significantly complementary features. Finally, the candidates were classified using a fully connected layer block. An exhaustive experiment on the LUNA16 challenge dataset was conducted to verify the effectiveness and performance of the proposed network. The AECS-CNN achieved a sensitivity of 0.92 with 4 false positives per scan. The results indicate that the attention mechanism can improve the network performance in false positive reduction, the proposed AECS-CNN can learn more representative features, and the attention module can guide the network to learn the discriminated feature channels and the crucial information embedded in the data, thereby effectively enhancing the performance of the detection system.

Ultrasound-guided hydrostatic enema reduction of intussusception and confirmation with single abdominal radiograph in children.

To detect false-positive reduction results after ultrasound (US)-guided hydrostatic intussusception reduction, we have incorporated water-soluble contrast material to the enema fluid and confirmed the reduction with a single abdominal radiograph. We present the results of the combined imaging method for the reduction of intussusception in children. The records of the patients who were treated for intussusception were analyzed retrospectively. Patients were divided into two groups: a US-guided reduction group and a US-guided reduction plus radiographic control group. The patient characteristics, symptoms, treatment methods, outcomes, and complications and follow-up were analyzed. A total of 164 intussusception episodes were treated in 153 patients. Hydrostatic reduction of intussusception was performed in 59 patients in the US-guided group and in 94 patients in the US-guided plus radiographic control group. Recurrence rate in the US-guided group was 15.7%, vs 3.5% in the US-guided plus radiography group (P = .029). In the US-guided plus radiographic control group, 5 (5.3%) false positive reductions under US guidance were determined by abdominal radiography. In order to decrease false positive reduction rate and early recurrence, US-guided intussusception reduction can be performed with saline plus water-soluble contrast material and confirmation of reduction obtained with a single direct abdominal radiograph.