- 标题
- 摘要
- 关键词
- 实验方案
- 产品
过滤筛选
- 2019
- 2018
- pattern recognition
- image
- partial discharge
- convolutional neural network(CNN)
- Conditional Random Fields (CRF)
- Convolutional Neural Network (CNN)
- Fine Classification
- Airborne hyperspectral
- green tide
- Elegant End-to-End Fully Convolutional Network (E3FCN)
- Optoelectronic Information Science and Engineering
- Shanghai Jiao Tong University
- Ocean University of China
- University of Oulu
- Wuhan University
- Central South University
- Hubei University
-
Deep Convolutional Neural Network for HEp-2 Fluorescence Intensity Classification
摘要： Indirect ImmunoFluorescence (IIF) assays are recommended as the gold standard method for detection of antinuclear antibodies (ANAs), which are of considerable importance in the diagnosis of autoimmune diseases. Fluorescence intensity analysis is very often complex, and depending on the capabilities of the operator, the association with incorrect classes is statistically easy. In this paper, we present a Convolutional Neural Network (CNN) system to classify positive/negative fluorescence intensity of HEp-2 IIF images, which is important for autoimmune diseases diagnosis. The method uses the best known pre-trained CNNs to extract features and a support vector machine (SVM) classifier for the final association to the positive or negative classes. This system has been developed and the classifier was trained on a database implemented by the AIDA (AutoImmunité, Diagnostic Assisté par ordinateur) project. The method proposed here has been tested on a public part of the same database, consisting of 2080 IIF images. The performance analysis showed an accuracy of fluorescent intensity around 93%. The results have been evaluated by comparing them with some of the most representative state-of-the-art works, demonstrating the quality of the system in the intensity classification of HEp-2 images.
关键词： autoimmune diseases,accuracy,SVM,receiver operating characteristic (ROC) curve,Convolutional Neural Network (CNN),IIF images
更新于2025-09-19 17:15:36
-
RAMS: Remote and automatic mammogram screening
摘要： About one in eight women in the U.S. will develop invasive breast cancer at some point in life. Breast cancer is the most common cancer found in women and if it is identified at an early stage by the use of mammograms, x-ray images of the breast, then the chances of successful treatment can be high. Typically, mammograms are screened by radiologists who determine whether a biopsy is necessary to ascertain the presence of cancer. Although historical screening methods have been effective, recent advances in computer vision and web technologies may be able to improve the accuracy, speed, cost, and accessibility of mammogram screenings. We propose a total screening solution comprised of three main components: a web service for uploading images and reviewing results, a machine learning algorithm for accepting or rejecting images as valid mammograms, and an artificial neural network for locating potential malignancies. Once an image is uploaded to our web service, an image acceptor determines whether or not the image is a mammogram. The image acceptor is primarily a one-class SVM built on features derived with a variational autoencoder. If an image is accepted as a mammogram, the malignancy identifier, a ResNet-101 Faster R-CNN, will locate tumors within the mammogram. On test data, the image acceptor had only 2 misclassifications out of 410 mammograms and 2 misclassifications out of 1,640 non-mammograms while the malignancy identifier achieved 0.951 AUROC when tested on BI-RADS 1, 5, and 6 images from the INbreast dataset.
关键词： Faster R-CNN,SVM,Deep Learning,DDSM,Convolutional,TensorFlow,INbreast,Mammograms,Telemedicine,Artificial Neural Network
更新于2025-09-19 17:15:36
-
Deep learning for FTIR histology: leveraging spatial and spectral features with convolutional neural networks
摘要： Current methods for cancer detection rely on tissue biopsy, chemical labeling/staining, and examination of the tissue by a pathologist. Though these methods continue to remain the gold standard, they are non-quantitative and susceptible to human error. Fourier transform infrared (FTIR) spectroscopic imaging has shown potential as a quantitative alternative to traditional histology. However, identification of histological components requires reliable classification based on molecular spectra, which are susceptible to artifacts introduced by noise and scattering. Several tissue types, particularly in heterogeneous tissue regions, tend to confound traditional classification methods. Convolutional neural networks (CNNs) are the current state-of-the-art in image classification, providing the ability to learn spatial characteristics of images. In this paper, we demonstrate that CNNs with architectures designed to process both spectral and spatial information can significantly improve classifier performance over per-pixel spectral classification. We report classification results after applying CNNs to data from tissue microarrays (TMAs) to identify six major cellular and acellular constituents of tissue, namely adipocytes, blood, collagen, epithelium, necrosis, and myofibroblasts. Experimental results show that the use of spatial information in addition to the spectral information brings significant improvements in the classifier performance and allows classification of cellular subtypes, such as adipocytes, that exhibit minimal chemical information but have distinct spatial characteristics. This work demonstrates the application and efficiency of deep learning algorithms in improving the diagnostic techniques in clinical and research activities related to cancer.
关键词： tissue classification,spatial features,convolutional neural networks,deep learning,spectral features,FTIR histology
更新于2025-09-19 17:15:36
-
RetoNet: a deep learning architecture for automated retinal ailment detection
摘要： Researchers are trying to tap the immense potential of big data to revolutionize all aspects of societal activity and to assist in having well informed decisions. Healthcare being one such field where proper analytics of available big medical data can lead to early detection and treatment of many ailments. Machine learning played a significant role in the design of automated diagnostic systems and today we have deep learning models in this arena which are outperforming human expertise in terms of predictive accuracy. This paper proposes RetoNet, a convolutional neural network architecture, which is trained and optimized to detect retinal ailment from fundus images with pronounced accuracy and its performance is also proven to be superior to a transfer learning based model developed for the same. Deep learning based e-diagnostic system can be an accurate, cost effective and convenient solution for the shortage of expertise on demand in the healthcare field.
关键词： Convolutional neural network,E-health,Retinal disease detection,ANN,Deep learning
更新于2025-09-19 17:15:36
-
Three-dimensional optical coherence tomography image denoising through multi-input fully-convolutional networks
摘要： In recent years, there has been a growing interest in applying convolutional neural networks (CNNs) to low-level vision tasks such as denoising and super-resolution. Due to the coherent nature of the image formation process, the optical coherence tomography (OCT) images are inevitably affected by noise. This paper proposes a new method named the multi-input fully-convolutional networks (MIFCN) for denoising of OCT images. In contrast to recently proposed natural image denoising CNNs, the proposed architecture allows the exploitation of high degrees of correlation and complementary information among neighboring OCT images through pixel by pixel fusion of multiple FCNs. The parameters of the proposed multi-input architecture are learned by considering the consistency between the overall output and the contribution of each input image. The proposed MIFCN method is compared with the state-of-the-art denoising methods adopted on OCT images of normal and age-related macular degeneration eyes in a quantitative and qualitative manner.
关键词： Multi-input FCN,Optical Coherence Tomography (OCT),Image denoising,Fully convolutional network (FCN)
更新于2025-09-19 17:15:36
-
Hierarchical spatial-aware Siamese network for thermal infrared object tracking
摘要： Most thermal infrared (TIR) tracking methods are discriminative, treating the tracking problem as a classification task. However, the objective of the classifier (label prediction) is not coupled to the objective of the tracker (location estimation). The classification task focuses on the between-class difference of the arbitrary objects, while the tracking task mainly deals with the within-class difference of the same objects. In this paper, we cast the TIR tracking problem as a similarity verification task, which is coupled well to the objective of the tracking task. We propose a TIR tracker via a Hierarchical Spatial-aware Siamese Convolutional Neural Network (CNN), named HSSNet. To obtain both spatial and semantic features of the TIR object, we design a Siamese CNN that coalesces the multiple hierarchical convolutional layers. Then, we propose a spatial-aware network to enhance the discriminative ability of the coalesced hierarchical feature. Subsequently, we train this network end to end on a large visible video detection dataset to learn the similarity between paired objects before we transfer the network into the TIR domain. Next, this pre-trained Siamese network is used to evaluate the similarity between the target template and target candidates. Finally, we locate the candidate that is most similar to the tracked target. Extensive experimental results on the benchmarks VOT-TIR 2015 and VOT-TIR 2016 show that our proposed method achieves favorable performance compared to the state-of-the-art methods.
关键词： Thermal infrared tracking,Similarity verification,Spatial-aware,Siamese convolutional neural network
更新于2025-09-19 17:15:36
-
Photoelectron spectroscopy of solvated dicarboxylate and alkali metal ion clusters, M <sup>+</sup> [O <sub/>2</sub> C(CH <sub/>2</sub> ) <sub/>2</sub> CO <sub/>2</sub> ] <sup>2?</sup> [H <sub/>2</sub> O] <sub/>n</sub> (M = Na, K; <i>n</i> = 1–6)
摘要： Image denoising is one of the most important directions in image processing. Medical images are often affected by noise and interference from the environment and equipment during acquisition, conversion, and transmission, resulting in degradation. This paper mainly introduces a new convolutional neural network structure for medical image denoising - deep neural network based on wavelet domain (deep wavelet denoising net DWDN). Our DWDN model exhibits high effectiveness in general medical image denoising tasks and is more excellent in the details of image.
关键词： denoising,wavelet transform,medical image,convolutional neural network
更新于2025-09-19 17:15:36
-
[IEEE 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Munich, Germany (2019.6.23-2019.6.27)] 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Extension of the Ion Optical Clock to Multi-Ion Systems
摘要： Deep neural networks are a branch in machine learning that has seen a meteoric rise in popularity due to its powerful abilities to represent and model high-level abstractions in highly complex data. One area in deep neural networks that are ripe for exploration is neural connectivity formation. A pivotal study on the brain tissue of rats found that synaptic formation for specific functional connectivity in neocortical neural microcircuits can be surprisingly well modeled and predicted as a random formation. Motivated by this intriguing finding, we introduce the concept of StochasticNet where deep neural networks are formed via stochastic connectivity between neurons. As a result, any type of deep neural networks can be formed as a StochasticNet by allowing the neuron connectivity to be stochastic. Stochastic synaptic formations in a deep neural network architecture can allow for efficient utilization of neurons for performing specific tasks. To evaluate the feasibility of such a deep neural network architecture, we train a StochasticNet using four different image datasets (CIFAR-10, MNIST, SVHN, and STL-10). Experimental results show that a StochasticNet using less than half the number of neural connections as a conventional deep neural network achieves comparable accuracy and reduces overfitting on the CIFAR-10, MNIST, and SVHN data sets. Interestingly, StochasticNet with less than half the number of neural connections, achieved a higher accuracy (relative improvement in test error rate of ～6% compared to ConvNet) on the STL-10 data set than a conventional deep neural network. Finally, the StochasticNets have faster operational speeds while achieving better or similar accuracy performances.
关键词： Deep convolutional neural network,random graph,StochasticNet
更新于2025-09-19 17:13:59
-
A convolutional neural network for prediction of laser power using melt-pool images in laser powder bed fusion
摘要： In laser powder bed fusion, a convolutional neural network could build a good regression model to predict a laser power value from a melt-pool image. To empirically validate it, we used the acquired image data from a monitoring system inside metal additive manufacturing equipment and optimally configured a convolutional network by the grid search of hyper-parameters. The proposed network showed only 0.12 % of test images were out of the criterion for judging the predicted laser power value to be reliable and showed more accurate results than deep feed-forward neural network in the prediction of laser power states unseen in training steps. We expect that the proposed model could be utilized to discover the problematic position in additive-manufactured layers causing defects during a process.
关键词： convolutional neural network,melt-pool image,process monitoring,metal additive manufacturing,laser powder bed fusion
更新于2025-09-19 17:13:59
-
CNN based automatic detection of photovoltaic cell defects in electroluminescence images
摘要： Automatic defect detection is gaining huge importance in photovoltaic (PV) field due to limited application of manual/visual inspection and rising production quantities of PV modules. This study is conducted for automatic detection of PV module defects in electroluminescence (EL) images. We presented a novel approach using light convolutional neural network architecture for recognizing defects in EL images which achieves state of the art results of 93.02 % on solar cell dataset of EL images. It requires less computational power and time. It can work on an ordinary CPU computer while maintaining real time speed. It takes only 8.07 milliseconds for predicting one image. For proposing light architecture, we perform extensive experimentation on series of architectures. Moreover, we evaluate data augmentation operations to deal with data scarcity. Overfitting appears a significant problem; thus, we adopt appropriate strategies to generalize model. The impact of each strategy is presented. In addition, cracking patterns and defects that can appear in EL images are reviewed; which will help to label new images appropriately for predicting specific defect types upon availability of large data. The proposed framework is experimentally applied in lab and can help for automatic defect detection in field and industry.
关键词： PV cell cracking,Automatic defect detection,Convolutional neural network (CNN),Electroluminescence,Deep learning,Photovoltaic (PV) modules
更新于2025-09-19 17:13:59