- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP) - Anaheim, CA, USA (2018.11.26-2018.11.29)] 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP) - DIGITAL STAINING OF HIGH-RESOLUTION FTIR SPECTROSCOPIC IMAGES
摘要： Histological stains, such as hemotaxylin and eosin (H&E), are commonly used to label tissue in clinical biopsies. However, these labels modify the tissue chemistry, making it difficult to use for further downstream analysis. Fourier transform infrared spectroscopy (FTIR) has shown promising results for characterizing disease-relevant tissues without chemical labels or dyes. However, tissue classification requires human annotation, which is difficult and tedious to acquire for complex samples. In addition, the results of a molecular analysis must be presented in a way that facilitates diagnosis for a trained pathologist. One proposed approach is digital staining, which uses machine learning to map an infrared spectroscopic image to the image that would be ideally produced with a chemical stain. While these methods produce promising results, the resolution is significantly lower than traditional histology. We demonstrate that high-resolution mappings can be obtained using FTIR imaging and histological staining of the same sample. In addition, we demonstrate that better results can be achieved with more recent convolutional neural networks (CNNs) that take advantage of both spatial and spectral features.
关键词： CNN,Digital staining,Image analysis,Histopathology
更新于2025-11-19 16:56:35
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Deep structure tensor graph search framework for automated extraction and characterization of retinal layers and fluid pathology in retinal SD-OCT scans
摘要： Maculopathy is a group of retinal disorders that affect macula and cause severe visual impairment if not treated in time. Many computer-aided diagnostic methods have been proposed over the past that automatically detect macular diseases. However, to our best knowledge, no literature is available that provides an end-to-end solution for analyzing healthy and diseased macular pathology. This paper proposes a vendor-independent deep convolutional neural network and structure tensor graph search-based segmentation framework (CNN-STGS) for the extraction and characterization of retinal layers and fluid pathology, along with 3-D retinal profiling. CNN-STGS works by first extracting nine layers from an optical coherence tomography (OCT) scan. Afterward, the extracted layers, combined with a deep CNN model, are used to automatically segment cyst and serous pathology, followed by the autonomous 3-D retinal profiling. CNN-STGS has been validated on publicly available Duke datasets (containing a cumulative of 42,281 scans from 439 subjects) and Armed Forces Institute of Ophthalmology dataset (containing 4,260 OCT scans of 51 subjects), which are acquired through different OCT machinery. The performance of the CNN-STGS framework is validated through the marked annotations, and it significantly outperforms the existing solutions in various metrics. The proposed CNN-STGS framework achieved a mean Dice coefficient of 0.906 for segmenting retinal fluids, along with an accuracy of 98.75% for characterizing cyst and serous fluid from diseased retinal OCT scans.
关键词： convolutional neural network (CNN),Optical coherence tomography (OCT),maculopathy,ophthalmology,graph search
更新于2025-09-23 15:23:52
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[IEEE 2018 IEEE 3rd International Conference on Signal and Image Processing (ICSIP) - Shenzhen, China (2018.7.13-2018.7.15)] 2018 IEEE 3rd International Conference on Signal and Image Processing (ICSIP) - Spacecraft Detection Based on Deep Convolutional Neural Network
摘要： Spacecraft detection is one of essential issues on aerospace information processing and control, and can provide reliable dynamic state of target, so as to support decisions made on target recognition, classification, catalogue, et al. Although numerous spacecraft detection methods exist, most of them cannot achieve real-time detection, and are still lack of better accuracy and fault-tolerance for different scenes. Recently, deep learning algorithms have achieved fantastic detection performance in computer vision community, especially the regression-based convolutional neural network YOLOv2, which has good accuracy and speed, and outperforming other state-of-the-art detection methods. This paper for the first time applies CNN to the detection of spacecraft and sets up a dataset for target detection in space. Our method starts with image annotation and data augmentation, and then uses our improved regression-based convolutional neural network YOLOv2 to detect spacecraft in an image. The experimental results have shown that our algorithm achieves 97.8% detection rate in the test set, and the average detection time of each image is about 0.018s, which has lower time overhead and better robustness to rotation and illumination changes of spacecraft.
关键词： Spacecraft,CNN,Target detection,YOLOv2
更新于2025-09-23 15:23:52
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Exploiting superior CNN-based iris segmentation for better recognition accuracy
摘要： CNN-based iris segmentations have been proven to be superior to traditional iris segmentation techniques in terms of segmentation error metrics. To properly utilize them in a traditional biometric recognition systems requires a parameterization of the iris, based on the generated segmentation, to obtain the normalised iris texture typically used for feature extraction. This is an unsolved problem. We will introduce a method to parameterize CNN based segmentation, bridging the gap between CNN based segmentation and the rubbersheet-transform. The parameterization enables the CNN segmentation as full segmentation step in any regular iris biometric system, or alternatively the segmentation can be utilized as a noise mask for other segmentation methods. Both of these options will be evaluated.
关键词： Iris segmentation,CNN,Parameterization of iris masks,Iris biometrics
更新于2025-09-23 15:23:52
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[IEEE 2018 IEEE 13th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP) - Aristi Village, Zagorochoria, Greece (2018.6.10-2018.6.12)] 2018 IEEE 13th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP) - Quantitative Evaluation of Salient Deep Neural Network Features Using Random Forests
摘要： The Deep Neural Networks and Deep Convolutional Neural Network have the property of providing multi-scale features at different layers of the network. Combination of these large number of features is one of the attributed reasons for the performance of the Neural Network (NN) on vision problems. This work uses Random Forests to identify robust features at various layers of the NN and evaluates the classification performance of these features in isolation. We propose a method for evaluation of parts of an already trained network using the selection by entropy maximization property of the Random Forests. We define measures for saliency in terms of contribution to the final classification, and evaluate the feature saliency. Simultaneously, a measure to identify the imperativeness of network features for classification is also formalized. The experiments made on a Hand dataset and the MNIST dataset, quantitatively validate various intuitions like the discriminatory nature of the outer layer features.
关键词： Random Forests,Feature Evaluation,CNN,Feature Selection
更新于2025-09-23 15:23:52
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SCN: Switchable Context Network for Semantic Segmentation of RGB-D Images
摘要： Context representations have been widely used to profit semantic image segmentation. The emergence of depth data provides additional information to construct more discriminating context representations. Depth data preserves the geometric relationship of objects in a scene, which is generally hard to be inferred from RGB images. While deep convolutional neural networks (CNNs) have been successful in solving semantic segmentation, we encounter the problem of optimizing CNN training for the informative context using depth data to enhance the segmentation accuracy. In this paper, we present a novel switchable context network (SCN) to facilitate semantic segmentation of RGB-D images. Depth data is used to identify objects existing in multiple image regions. The network analyzes the information in the image regions to identify different characteristics, which are then used selectively through switching network branches. With the content extracted from the inherent image structure, we are able to generate effective context representations that are aware of both image structures and object relationships, leading to a more coherent learning of semantic segmentation network. We demonstrate that our SCN outperforms state-of-the-art methods on two public datasets.
关键词： Context representation,convolutional neural network (CNN),RGB-D images,semantic segmentation
更新于2025-09-23 15:23:52
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A CNN With Multiscale Convolution and Diversified Metric for Hyperspectral Image Classification
摘要： Recently, researchers have shown the powerful ability of deep methods with multilayers to extract high-level features and to obtain better performance for hyperspectral image classification. However, a common problem of traditional deep models is that the learned deep models might be suboptimal because of the limited number of training samples, especially for the image with large intraclass variance and low interclass variance. In this paper, novel convolutional neural networks (CNNs) with multiscale convolution (MS-CNNs) are proposed to address this problem by extracting deep multiscale features from the hyperspectral image. Moreover, deep metrics usually accompany with MS-CNNs to improve the representational ability for the hyperspectral image. However, the usual metric learning would make the metric parameters in the learned model tend to behave similarly. This similarity leads to obvious model’s redundancy and, thus, shows negative effects on the description ability of the deep metrics. Traditionally, determinantal point process (DPP) priors, which encourage the learned factors to repulse from one another, can be imposed over these factors to diversify them. Taking advantage of both the MS-CNNs and DPP-based diversity-promoting deep metrics, this paper develops a CNN with multiscale convolution and diversified metric to obtain discriminative features for hyperspectral image classification. Experiments are conducted over four real-world hyperspectral image data sets to show the effectiveness and applicability of the proposed method. Experimental results show that our method is better than original deep models and can produce comparable or even better classification performance in different hyperspectral image data sets with respect to spectral and spectral–spatial features.
关键词： deep metric learning,determinantal point process (DPP),image classification,multiscale features,Convolutional neural network (CNN),hyperspectral image
更新于2025-09-23 15:23:52
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[Lecture Notes in Computational Vision and Biomechanics] Computer Aided Intervention and Diagnostics in Clinical and Medical Images Volume 31 || Deep Neural Architecture for Localization and Tracking of Surgical Tools in Cataract Surgery
摘要： Over the last couple of decades, the quality of surgical interventions has improved owing to the use of computer vision and robotic assistance. One such application of computer vision, namely, detection of surgical tools in videos is gaining attention of the medical image processing community. The main motivation for detection, localization, and annotation of surgical tools is to develop applications for surgical workflow analysis. Such an analysis can aid in report generation, real-time decision support, etc. Cataract surgery is one of the common surgical procedure where surgeons do have direct visual access to the surgical site. Extremely small tools are used for this procedure and the surgeons observe the surgical site through a surgical microscope. In such cases, detecting the presence of tools can act an additional aid to the surgeon as well as other surgical staffs. We propose a framework consisting of a Convolutional Neural Network (CNN) which learns to distinguish and detect the presence of various surgical tools by learning robust features from the frames of a surgical video. Various deep neural architectures are hence evaluated for the task of detecting tools. The baseline models used for the purpose are pretrained on Imagenet dataset and they render upto 50% prediction accuracy. All the experiments have been validated on the dataset released as part of the Cataracts Grand Challenge. A framework for localization and detection of tools has also been proposed, which is capable of extracting visual features from glimpses of an image, by adaptively selecting and processing only the selected regions at high resolution.
关键词： Multiple tool detection,Cataract surgery,CNN,Glimpse network,Deep neural architectures,Class imbalance
更新于2025-09-23 15:23:52
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[IEEE 2018 24th International Conference on Pattern Recognition (ICPR) - Beijing (2018.8.20-2018.8.24)] 2018 24th International Conference on Pattern Recognition (ICPR) - An Efficient Approach for Polyps Detection in Endoscopic Videos Based on Faster R-CNN
摘要： Polyps have long been considered as one of the major etiologies to colorectal cancer which is a fatal disease around the world, thus early detection and recognition of polyps plays a crucial role in clinical routines. Accurate diagnoses of polyps through endoscopes operated by physicians becomes a challenging task not only due to the varying expertise of physicians, but also the inherent nature of endoscopic inspections. To facilitate this process, computer-aid techniques that emphasize on fully-conventional image processing and novel machine learning enhanced approaches have been dedicatedly designed for polyp detection in endoscopic videos or images. Among all proposed algorithms, deep learning based methods take the lead in terms of multiple metrics in evolutions for algorithmic performance. In this work, a highly effective model, namely the faster region-based convolutional neural network (Faster R-CNN) is implemented for polyp detection. In comparison with the reported results of the state-of-the-art approaches on polyps detection, extensive experiments demonstrate that the Faster R-CNN achieves very competing results, and it is an efficient approach for clinical practice.
关键词： computer-aided diagnosis,deep learning,Faster R-CNN,polyp detection,endoscopic videos
更新于2025-09-23 15:23:52
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[IEEE 2018 3rd International Conference on Mechanical, Control and Computer Engineering (ICMCCE) - Huhhot (2018.9.14-2018.9.16)] 2018 3rd International Conference on Mechanical, Control and Computer Engineering (ICMCCE) - A Remote Sensing Image Key Target Recognition System Design Based on Faster R-CNN
摘要： Aiming at the problem of traditional low-level recognition of key targets in remote sensing images, a method for target detection and recognition based on Faster R-CNN is proposed. Firstly, the open source remote sensing image data set NWPU VHR-10 dataset is converted into VOC 2007 format as the training sets and test sets. Secondly, according to the training set category information, the hyper-parameters of the neural network are refined, and then the training set is trained using the Faster R-CNN neural network to generate a model. Finally, this model is used to detect unknown remote sensing images and identify important targets. The simulation results show that the method has high recognition accuracy and speed, and can provide reference for recognition of the key targets of remote sensing images.
关键词： Faster R-CNN,convolution neural network,deep learning,key target recognition,remote sensing image detection
更新于2025-09-23 15:22:29