Medical wastes bring probable hazards and public risks if not handled correctly, especially during the covid-19 pandemic. Waste management costs are rising due to continues devastation of covid-19 virus. Increased car...
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Placental lesions indicative of maternal vascular malperfusion (MVM) are associated with future cardiovascular disease (CVD) in women with placenta-mediated diseases of pregnancy. Early diagnosis of CVD can reduce mor...
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ISBN:
(数字)9798350380903
ISBN:
(纸本)9798350380910
Placental lesions indicative of maternal vascular malperfusion (MVM) are associated with future cardiovascular disease (CVD) in women with placenta-mediated diseases of pregnancy. Early diagnosis of CVD can reduce morbidity, mortality, and healthcare costs. MVM lesions can be detected in placental histopathology images, providing a means for CVD risk screening postnatally. Deep learning approaches, such as convolutional neural networks (CNNs), have demonstrated high potential for automating histopathology image analysis. Given the large size of histopathology whole slide images (WSIs), a patch-based approach is often employed; however, labeling is typically only available at the WSI level. MVM lesions can present heterogeneously across the image, so assigning WSI labels to patches results in patch mislabeling. In this study, we propose a weakly supervised learning method for MVM lesion classification. Features were computed from the patches extracted from WSIs using the CNN-based Resnet18 pre-trained model. An attention-based network, using weakly supervised learning, has been developed to classify WSIs as MVM+/-. The model performance was assessed against a baseline model, which was a patch-based fully supervised model. The weakly supervised learning method had an accuracy of 87.4%, which was superior to the baseline model accuracy of 81.1%. This is a promising result that suggests that weakly supervised learning can help overcome patch labeling errors that arise from the heterogeneous presentation of histopathology features, such as MVM lesions, and only having WSI-level labels.
Although supervised image denoising networks have shown remarkable performance on synthesized noisy images, they often fail in practice due to the difference between real and synthesized noise. Since clean-noisy image...
Although supervised image denoising networks have shown remarkable performance on synthesized noisy images, they often fail in practice due to the difference between real and synthesized noise. Since clean-noisy image pairs from the real world are extremely costly to gather, self-supervised learning, which utilizes noisy input itself as a target, has been studied. To prevent a self-supervised denoising model from learning identical mapping, each output pixel should not be influenced by its corresponding input pixel; This requirement is known as J-invariance. Blind-spot networks (BSNs) have been a prevalent choice to ensure J-invariance in self-supervised image denoising. However, constructing variations of BSNs by injecting additional operations such as downsampling can expose blinded information, thereby violating J-invariance. Consequently, convolutions designed specifically for BSNs have been allowed only, limiting architectural flexibility. To overcome this limitation, we propose PUCA, a novel J-invariant U-Net architecture, for self-supervised denoising. PUCA leverages patch-unshuffle/shuffle to dramatically expand receptive fields while maintaining J-invariance and dilated attention blocks (DABs) for global context incorporation. Experimental results demonstrate that PUCA achieves state-of-the-art performance, outperforming existing methods in self-supervised image denoising.
Fish image classification presents an intriguing challenge in the field of computer vision. This research aims to develop an accurate classification model to differentiate between four different fish species using a c...
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The aim of continual learning is to learn new tasks continuously (i.e., plasticity) without forgetting previously learned knowledge from old tasks (i.e., stability). In the scenario of online continual learning, where...
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Optimal planning of distributed generation (DG) units within power system networks PSN is crucial for improving the reliability and quality of power delivery. This involves optimizing the size and location of DGs, con...
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ISBN:
(数字)9798350379648
ISBN:
(纸本)9798350379655
Optimal planning of distributed generation (DG) units within power system networks PSN is crucial for improving the reliability and quality of power delivery. This involves optimizing the size and location of DGs, considering both technical and operational constraints. This study proposes the Quadratic Interpolation Optimization (QIO) method, a mathematically inspired meta-heuristic algorithm, for the optimal planning and allocation of renewable energy (RE)-based DG units. The optimization problem aims to enhance the voltage profile and stability index of the network, minimize power losses, and maximize investment profitability. The QIO algorithm utilizes the principle that any three points can define a quadratic function, efficiently identifying the minimizer of that function and overcoming the limitations of traditional quadratic interpolation methods. To validate the proposed algorithm, simulations were conducted on the standard IEEE 69-bus radial distribution system. Various scenarios were analyzed by varying the type and number of DG units to evaluate their impact on network performance. Compared to existing methods, the QIO demonstrates superior efficiency in optimizing the allocation and sizing of RE-based DG units, achieving faster convergence and reduced computational time.
This paper proposes a first-order optimization framework for nonlinear optimal control problems, efficiently handling complex dynamics via projection onto a lifted, approximately linear constraint manifold constructed...
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Successful detection of Out-of-Distribution (OoD) data is becoming increasingly important to ensure safe deployment of neural networks. One of the main challenges in OoD detection is that neural networks output overco...
Successful detection of Out-of-Distribution (OoD) data is becoming increasingly important to ensure safe deployment of neural networks. One of the main challenges in OoD detection is that neural networks output overconfident predictions on OoD data, make it difficult to determine OoD-ness of data solely based on their predictions. Outlier exposure addresses this issue by introducing an additional loss that encourages low-confidence predictions on OoD data during training. While outlier exposure has shown promising potential in improving OoD detection performance, all previous studies on outlier exposure have been limited to utilizing visual outliers. Drawing inspiration from the recent advancements in vision-language pre-training, this paper venture out to the uncharted territory of textual outlier exposure. First, we uncover the benefits of using textual outliers by replacing real or virtual outliers in the image-domain with textual equivalents. Then, we propose various ways of generating preferable textual outliers. Our extensive experiments demonstrate that generated textual outliers achieve competitive performance on large-scale OoD and hard OoD benchmarks. Furthermore, we conduct empirical analyses of textual outliers to provide primary criteria for designing advantageous textual outliers: near-distribution, descriptiveness, and inclusion of visual semantics. Code is available at https://***/wiarae/TOE
In real world applications of multiclass classification models, misclassification in an important class (e.g., stop sign) can be significantly more harmful than in other classes (e.g., no parking). Thus, it is crucial...
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