- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
Deep learning enables cross-modality super-resolution in fluorescence microscopy
摘要： We present deep-learning-enabled super-resolution across different fluorescence microscopy modalities. This data-driven approach does not require numerical modeling of the imaging process or the estimation of a point-spread-function, and is based on training a generative adversarial network (GAN) to transform diffraction-limited input images into super-resolved ones. Using this framework, we improve the resolution of wide-field images acquired with low-numerical-aperture objectives, matching the resolution that is acquired using high-numerical-aperture objectives. We also demonstrate cross-modality super-resolution, transforming confocal microscopy images to match the resolution acquired with a stimulated emission depletion (STED) microscope. We further demonstrate that total internal reflection fluorescence (TIRF) microscopy images of subcellular structures within cells and tissues can be transformed to match the results obtained with a TIRF-based structured illumination microscope. The deep network rapidly outputs these super-resolved images, without any iterations or parameter search, and could serve to democratize super-resolution imaging.
关键词： GAN,cross-modality,super-resolution,fluorescence microscopy,deep learning
更新于2025-11-21 11:24:58
-
Dictionaries of deep features for land-use scene classification of very high spatial resolution images
摘要： Land-use classification in very high spatial resolution images is critical in the remote sensing field. Consequently, remarkable efforts have been conducted towards developing increasingly accurate approaches for this task. In recent years, deep learning has emerged as a dominant paradigm for machine learning, and methodologies based on deep convolutional neural networks have received particular attention from the remote sensing community. These methods typically utilize transfer learning and/or data augmentation to accommodate a small number of labeled images in the publicly available datasets in this field. However, they typically require powerful computers and/or a long time for training. In this work, we propose a simple and novel method for land-use classification in very high spatial resolution images, which efficiently combines transfer learning with a sparse representation. Specifically, the proposed method performs the classification of land-use scenes using a modified version of the well-known sparse representation-based classification method. While this method directly uses the training images to form dictionaries, which are employed to classify test images, our method utilizes a pre-trained deep convolutional neural network and the Gaussian mixture model to generate more robust and compact 'dictionaries of deep features.' The effectiveness of the proposed method was evaluated on two publicly available datasets: UC Merced and Brazilian Cerrado–Savana. The experimental results suggest that our method can potentially outperform state-of-the-art techniques for land-use classification in very high spatial resolution images.
关键词： Dictionary learning,Land-use classification,Sparse representation,Feature learning,Deep learning
更新于2025-09-23 15:23:52
-
Unsupervised Learning Based Fast Beamforming Design for Downlink MIMO
摘要： In the downlink transmission scenario, power allocation and beamforming design at the transmitter are essential when using multiple antenna arrays. This paper considers a multiple input-multiple output broadcast channel to maximize the weighted sum-rate under the total power constraint. The classical weighted minimum mean-square error (WMMSE) algorithm can obtain suboptimal solutions but involves high computational complexity. To reduce this complexity, we propose a fast beamforming design method using unsupervised learning, which trains the deep neural network (DNN) offline and provides real-time service online only with simple neural network operations. The training process is based on an end-to-end method without labeled samples avoiding the complicated process of obtaining labels. Moreover, we use the 'APoZ'-based pruning algorithm to compress the network volume, which further reduces the computational complexity and volume of the DNN, making it more suitable for low computation-capacity devices. Finally, experimental results demonstrate that the proposed method improves computational speed significantly with performance close to the WMMSE algorithm.
关键词： beamforming,unsupervised learning,deep learning,network pruning,MIMO
更新于2025-09-23 15:23:52
-
An enhanced diabetic retinopathy detection and classification approach using deep convolutional neural network
摘要： The objective of this study is to propose an alternative, hybrid solution method for diagnosing diabetic retinopathy from retinal fundus images. In detail, the hybrid method is based on using both image processing and deep learning for improved results. In medical image processing, reliable diabetic retinopathy detection from digital fundus images is known as an open problem and needs alternative solutions to be developed. In this context, manual interpretation of retinal fundus images requires the magnitude of work, expertise, and over-processing time. So, doctors need support from imaging and computer vision systems and the next step is widely associated with use of intelligent diagnosis systems. The solution method proposed in this study includes employment of image processing with histogram equalization, and the contrast limited adaptive histogram equalization techniques. Next, the diagnosis is performed by the classification of a convolutional neural network. The method was validated using 400 retinal fundus images within the MESSIDOR database, and average values for different performance evaluation parameters were obtained as accuracy 97%, sensitivity (recall) 94%, specificity 98%, precision 94%, FScore 94%, and GMean 95%. In addition to those results, a general comparison of with some previously carried out studies has also shown that the introduced method is efficient and successful enough at diagnosing diabetic retinopathy from retinal fundus images. By employing the related image processing techniques and deep learning for diagnosing diabetic retinopathy, the proposed method and the research results are valuable contributions to the associated literature.
关键词： Image processing,Deep learning,Convolutional neural network,Diabetic retinopathy
更新于2025-09-23 15:23:52
-
Focal Boundary Guided Salient Object Detection
摘要： The performance of salient object segmentation has been significantly advanced by using deep convolutional networks. However, these networks often produce blob-like saliency maps without accurate object boundaries. This is caused by the limited spatial resolution of their feature maps after multiple pooling operations, and might hinder downstream applications that require precise object shapes. To address this issue, we propose a novel deep model—Focal Boundary Guided (Focal-BG) network. Our model is designed to jointly learn to segment salient object masks and detect salient object boundaries. Our key idea is that additional knowledge about object boundaries can help to precisely identify the shape of the object. Moreover, our model incorporates a refinement pathway to refine the mask prediction, and makes use of the focal loss to facilitate the learning of the hard boundary pixels. To evaluate our model, we conduct extensive experiments. Our Focal-BG network consistently outperforms state-of-the-art methods on five major benchmarks. We provide a detailed analysis of these results and demonstrate that our joint modeling of salient object boundary and mask helps to better capture shape details, especially in the vicinity of object boundaries.
关键词： Salient Object Segmentation,Deep Learning,Visual Saliency Detection,Boundary Detection
更新于2025-09-23 15:23:52
-
Fast ScanNet: Fast and Dense Analysis of Multi-Gigapixel Whole-Slide Images for Cancer Metastasis Detection
摘要： Lymph node metastasis is one of the most important indicators in breast cancer diagnosis, that is traditionally observed under the microscope by pathologists. In recent years, with the dramatic advance of high-throughput scanning and deep learning technology, automatic analysis of histology from whole-slide images has received a wealth of interest in the field of medical image computing, which aims to alleviate pathologists’ workload and simultaneously reduce misdiagnosis rate. However, automatic detection of lymph node metastases from whole-slide images remains a key challenge because such images are typically very large, where they can often be multiple gigabytes in size. Also, the presence of hard mimics may result in a large number of false positives. In this paper, we propose a novel method with anchor layers for model conversion, which not only leverages the efficiency of fully convolutional architectures to meet the speed requirement in clinical practice, but also densely scans the whole-slide image to achieve accurate predictions on both micro- and macro-metastases. Incorporating the strategies of asynchronous sample prefetching and hard negative mining, the network can be effectively trained. The efficacy of our method are corroborated on the benchmark dataset of 2016 Camelyon Grand Challenge. Our method achieved significant improvements in comparison with the state-of-the-art methods on tumour localization accuracy with a much faster speed and even surpassed human performance on both challenge tasks.
关键词： metastasis detection,Histopathology image analysis,deep learning,whole-slide image,computational pathology
更新于2025-09-23 15:23:52
-
[SPIE Image Processing - Houston, United States (2018.2.10-2018.2.15)] Medical Imaging 2018: Image Processing - Deep learning for biomarker regression: application to osteoporosis and emphysema on chest CT scans
摘要： Introduction: Biomarker computation using deep-learning often relies on a two-step process, where the deep learning algorithm segments the region of interest and then the biomarker is measured. We propose an alternative paradigm, where the biomarker is estimated directly using a regression network. We showcase this image-to-biomarker paradigm using two biomarkers: the estimation of bone mineral density (BMD) and the estimation of lung percentage of emphysema from CT scans. Materials and methods: We use a large database of 9,925 CT scans to train, validate and test the network for which reference standard BMD and percentage emphysema have been already computed. First, the 3D dataset is reduced to a set of canonical 2D slices where the organ of interest is visible (either spine for BMD or lungs for emphysema). This data reduction is performed using an automatic object detector. Second, The regression neural network is composed of three convolutional layers, followed by a fully connected and an output layer. The network is optimized using a momentum optimizer with an exponential decay rate, using the root mean squared error as cost function. Results: The Pearson correlation coefficients obtained against the reference standards are r = 0.940 (p < 0.00001) and r = 0.976 (p < 0.00001) for BMD and percentage emphysema respectively. Conclusions: The deep-learning regression architecture can learn biomarkers from images directly, without indicating the structures of interest. This approach simplifies the development of biomarker extraction algorithms. The proposed data reduction based on object detectors conveys enough information to compute the biomarkers of interest.
关键词： regression,deep learning,bone mineral density,computed tomography,emphysema
更新于2025-09-23 15:23:52
-
FusionCNN: a remote sensing image fusion algorithm based on deep convolutional neural networks
摘要： In remote sensing image fusion field, traditional algorithms based on the human-made fusion rules are severely sensitive to the source images. In this paper, we proposed an image fusion algorithm using convolutional neural networks (FusionCNN). The fusion model implicitly represents a fusion rule whose inputs are a pair of source images and the output is a fused image with end-to-end property. As no datasets can be used to train FusionCNN in remote sensing field, we constructed a new dataset from a natural image set to approximate MS and Pan images. In order to obtain higher fusion quality, low frequency information of MS is used to enhance the Pan image in the pre-processing step. The method proposed in this paper overcomes the shortcomings of the traditional fusion methods in which the fusion rules are artificially formulated, because it learns an adaptive strong robust fusion function through a large amount of training data. In this paper, Landsat and Quickbird satellite data are used to verify the effectiveness of the proposed method. Experimental results show that the proposed fusion algorithm is superior to the comparative algorithms in terms of both subjective and objective evaluation.
关键词： Convolutional neural networks,Deep learning,Remote sensing image fusion,Image enhancement
更新于2025-09-23 15:23:52
-
DeepPrimitive: Image decomposition by layered primitive detection
摘要： The perception of the visual world through basic building blocks, such as cubes, spheres, and cones, gives human beings a parsimonious understanding of the visual world. Thus, efforts to find primitive-based geometric interpretations of visual data date back to 1970s studies of visual media. However, due to the difficulty of primitive fitting in the pre-deep learning age, this research approach faded from the main stage, and the vision community turned primarily to semantic image understanding. In this paper, we revisit the classical problem of building geometric interpretations of images, using supervised deep learning tools. We build a framework to detect primitives from images in a layered manner by modifying the YOLO network; an RNN with a novel loss function is then used to equip this network with the capability to predict primitives with a variable number of parameters. We compare our pipeline to traditional and other baseline learning methods, demonstrating that our layered detection model has higher accuracy and performs better reconstruction.
关键词： biologically inspired vision,primitive detection,deep learning,layered image decomposition
更新于2025-09-23 15:23:52
-
Fringe pattern denoising based on deep learning
摘要： In this paper, deep learning as a novel algorithm is proposed to reduce the noise of the fringe patterns. Usually, the training samples are acquired through experimental acquisition, but these data can be easily obtained by simulations in the proposed algorithm. Thus, the time cost used for the whole training process is greatly reduced. The performance of the proposed algorithm has been demonstrated through the analysis on the simulated and real fringe patterns. It is obvious that the proposed algorithm has a faster calculation speed compared with existing denoising algorithm, and recovers the fringe patterns with high quality. Most importantly, the proposed algorithm may provide a solution to other denoising problems in the field of optics, such as hologram and speckle denoising.
关键词： Fringe pattern,Deep learning,Denoising
更新于2025-09-23 15:23:52