Deep learning for automated cell imaging analysis has become a tool of choice to process large amounts of data. But many of these methods lack explainability, slowing down their deployment for tasks such as diagnosis....
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ISBN:
(纸本)9798350349405;9798350349399
Deep learning for automated cell imaging analysis has become a tool of choice to process large amounts of data. But many of these methods lack explainability, slowing down their deployment for tasks such as diagnosis. We present a prototype-based framework to analyze structural changes which addresses the specific challenges of explainability in the context of cell imaging. Our method relies on classification between two distinct cell populations in a weakly supervised context where no label for individual cells is available. Our model extracts typical features from each population, representing intra-cellular structure, and provides an explanation on its classification decision by creating visualization of the local textures corresponding to the structures of interest. We show a real application where it effectively highlights a change in the organization of the actin content of the cells.
At present, deep learning has made impressive achievements in various fields;however, effectively training deep neural networks on small data sets remains a significant challenge. Transfer learning, as a method of eff...
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ISBN:
(纸本)9798350344868;9798350344851
At present, deep learning has made impressive achievements in various fields;however, effectively training deep neural networks on small data sets remains a significant challenge. Transfer learning, as a method of efficient training across multiple tasks, has been widely used to solve this problem. However, when the domain gap or the data volume difference between the two tasks is too large, the transfer learning may not perform well, and other optimization methods will be required to improve the performance. In this paper, we propose a new transfer learning method guided by the direction of objective optimization from the perspective of gradient. This method guides the gradient direction of the source task towards the gradient direction of the target task. In several similar and conflicting tasks, this method has achieved good results in efficiency and performance. In comparison with other transfer learning methods, the results shown by this method are generally better.
Super-resolution (SR) is an ill-posed inverse problem which consists in proposing high-resolution images consistent with a given low-resolution one. While most SR algorithms are deterministic, stochastic SR deals with...
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Deep neural networks (DNNs) have well-documented merits in learning nonlinear functions in high-dimensional spaces. stochastic gradient descent (SGD)-type optimization algorithms are the 'workhorse' for traini...
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ISBN:
(纸本)9781665405409
Deep neural networks (DNNs) have well-documented merits in learning nonlinear functions in high-dimensional spaces. stochastic gradient descent (SGD)-type optimization algorithms are the 'workhorse' for training DNNs. Nonetheless, such algorithms often suffer from slow convergence, sizable fluctuations, and abundant local solutions, to name a few. In this context, the present paper draws ideas from adaptive control of dynamical systems, and develops an adaptive proportional-integral-derivative (AdaPID) solver for fast, stable, and effective training of DNNs. AdaPID relies on second-order moment estimates of gradients to adaptively adjust the PID coefficients. Numerical tests corroborate the merits of AdaPID on several tasks such as image generation using generative adversarial networks (GANs) and image classification using convolutional neural networks (CNNs) as well as long-short term memories (LSTMs).
Computer-aided diagnosis (CAD) systems based on ultrasound have been developed and widely promoted in breast cancer screening. Due to the characteristics of low contrast and speckle noises, breast ultrasound image seg...
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Computer-aided diagnosis (CAD) systems based on ultrasound have been developed and widely promoted in breast cancer screening. Due to the characteristics of low contrast and speckle noises, breast ultrasound image segmentation, one of the crucial steps of CAD systems, has always been challenging. Recently, the emerging Transformer-based medical segmentation methods, which have a better ability to model long dependencies than convolutional neural networks (CNNs), have shown significant value for medical image segmentation. However, due to the limited data with the high-quality label, Transformer performs weakly on breast ultrasound image segmentation without pretraining. Thus, we propose the Attention-Gate Medical Transformer (AGMT) for small breast ultrasound datasets, which introduces the attention-gate (AG) module to suppress background information and the average radial derivative increment (Delta ARD) loss function to enhance shape information. We evaluate the AGMT on both a private dataset A and a public dataset B. On dataset A, the AGMT outperforms MT on the metrics of true positive ratio, jaccard index (JI) and dice similarity coefficient (DSC) by 6.4%, 2.3% and 1.9%, respectively. Meanwhile, when compared with UNet, the AGMT improves JI and DSC by 5.3% and 4.9%, respectively. The results show performance has significantly improved compared with mainstream models. In addition, we also conduct ablation experiments on the AG module and Delta ARD, which prove their effectiveness.
In this work, a deep convolutional neural network is proposed to improve the registration of microtopographic data. For this purpose, different mechanical surfaces were optically measured using a confocal laser scanni...
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ISBN:
(纸本)9781510657229
In this work, a deep convolutional neural network is proposed to improve the registration of microtopographic data. For this purpose, different mechanical surfaces were optically measured using a confocal laser scanning microscope. A wide range of surfaces with different materials, processingmethods, and topographic properties, such as isotropy and anisotropy or stochastic and deterministic features, are included. Training and testing datasets with known homographies are generated from these measurements by cropping a fixed and moving image patch from each topography and then randomly perturbing the latter. A pseudo-siamese network architecture based on the VGG Net is then used to predict these homographies. The network is trained with a supervised learning approach where the Euclidean distance between the predicted and the ground truth gives the loss function. The 4-point homography parameterization is used to improve the loss convergence. Furthermore, different amounts of image noise are added to enhance the prediction's robustness and prevent overfitting. The effectiveness of the proposed method is evaluated through different experiments. First, the network performance is compared to intensity-based and feature-based conventional registration algorithms regarding the resulting error, the noise-robustness, and the processing speed. In addition, images from the Microsoft Common Objects in Context (COCO) dataset are used to verify the network's generalization capability to new image types and contents. The results show that the learning-based approach offers much higher robustness regarding image noise and a much lower processing time. In contrast, conventional algorithms have a smaller registration error without image noise.
In this paper, we propose a system for 3D face model reconstruction. Earlier studies on reconstruction methods included the software modeling methods or the instrument scanning modeling methods. But both of the above ...
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ISBN:
(纸本)9781728198354
In this paper, we propose a system for 3D face model reconstruction. Earlier studies on reconstruction methods included the software modeling methods or the instrument scanning modeling methods. But both of the above methods require a lot of development resources and time costs. Therefore, we develop a reconstruction system using a weakly supervised approach combining Convolutional neural Networks (CNN) and 3D Morphable Face Models (3DMM). Given a sufficient number of 2D face images to train and learn the main features of the face, our system is capable of rapidly constructing 3D face models. The proposed method enhances the efficiency of preprocessing and improves the performance of loss function through image depth feature extraction and regression coefficients. Using two datasets for model evaluation and analysis, this study efficiently reconstructs faces without ground-truth labels.
In this paper, we propose a monocular 3D pose estimation method which explicitly takes into account the angles between the camera optical axis and bones (camera-bone angles) as well as temporal information. The propos...
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ISBN:
(纸本)9798350344868;9798350344851
In this paper, we propose a monocular 3D pose estimation method which explicitly takes into account the angles between the camera optical axis and bones (camera-bone angles) as well as temporal information. The proposed method combines a 2D-to-3D-based method, which predicts a 3D pose from a sequence of 2D poses, and convolutional neural network (CNN) and includes novel regularization loss to enable the CNN to extract camera-bone-angle information. The camera-bone-angle and temporal information suppress ambiguity of 2D-to-3D-based methods where the same 2D pose can be mapped to multiple 3D poses. Experiments on the Human3.6M and MPI-INF-3DHP datasets showed that the proposed method improved the performance by 5.1 mm and 2.1 mm in terms of mean per joint position error (MPJPE) respectively.
image compression and denoising represent fundamental challenges in imageprocessing with many real-world applications. To address practical demands, current solutions can be categorized into two main strategies: 1) s...
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ISBN:
(纸本)9798350390155;9798350390162
image compression and denoising represent fundamental challenges in imageprocessing with many real-world applications. To address practical demands, current solutions can be categorized into two main strategies: 1) sequential method;and 2) joint method. However, sequential methods have the disadvantage of error accumulation as there is information loss between multiple individual models. Recently, the academic community began to make some attempts to tackle this problem through end-to-end joint methods. Most of them ignore that different regions of noisy images have different characteristics. To solve these problems, in this paper, our proposed signal-to-noise ratio (SNR) aware joint solution exploits local and non-local features for image compression and denoising simultaneously. We design an end-to-end trainable network, which includes the main encoder branch, the guidance branch, and the signal-to-noise ratio (SNR) aware branch. We conducted extensive experiments on both synthetic and real-world datasets, demonstrating that our joint solution outperforms existing state-of-the-art methods.
This research aims to optimize the deep learning network by combining three-dimensional imaging technology and multidimensional signalprocessingmethods to improve the processing capabilities of complex three-dimensi...
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