Radio Frequency Identification (RFID) technology is greatly supporting a variety of life and industry. Enhanced by sensors, RFID tags can monitor real-time information about attached objects and the environment, makin...
详细信息
Diffeomorphic registration plays a crucial role in medical image analysis due to the invertible and one-to-one mapping transformation. In recent years, with the development of deep learning technology, convolutional n...
Diffeomorphic registration plays a crucial role in medical image analysis due to the invertible and one-to-one mapping transformation. In recent years, with the development of deep learning technology, convolutional neural networks (CNNs) have been a broad focus of research in medical image registration, and CNN-based methods have made great progress. However, the results of most existing methods generally are not necessarily diffeomorphic, generating implausibly bijective mappings between images due to the interpolation and discrete representation. Furthermore, the performances of CNNs may be limited by a lack of precise comprehension of global and long-range cross-image spatial relevance. Vision Transformer (ViT) is capable of enhancing the long-distance information interaction ability to identify the semantically anatomically correspondences of medical images. Compared with CNN, ViT has weak local feature extraction ability due to less inductive bias, especially in small-scale training datasets, meaning that the samples between adjacent pixels cannot be exploited adequately. To address the above challenges, we propose a novel Inverse-Consistent Convolutional Vision Transformer (IC-CViT) network for diffeomorphic image registration. Specifically, image pairs can explicitly conduct bi-directional registration through the predicted deformation filed, generated within the diffeomorphic mappings space and restricted by the proposed inverse consistent loss term. We verify our method on two 3D brain MRI scan datasets including OASIS and LPBA40. Comprehensive results demonstrate that IC-CViT achieves state-of-the-art registration accuracy while maintaining desired diffeomorphic properties.
In this paper, we propose a method to predict the success of primer amplification based on the relationship existing between the sequence of primer and template, which can optimize the primer design and select the pri...
In this paper, we propose a method to predict the success of primer amplification based on the relationship existing between the sequence of primer and template, which can optimize the primer design and select the primer with better amplification from the candidate primer set. The double-stranded structure between primer and template nucleotide sequences is represented here by a number of words, each consisting of five characters that form sentences, as the dataset for the experiment, which is learned using an attention-based mechanism of bidirectional long short-term memory neural network model (Attention-BiLSTM), and then predicts primer amplification. The model predicted the results of polymerase chain reaction (PCR) involving specific primers and specific DNA templates with 82% accuracy, an improvement of about 2% over the performance of the LSTM with more stable value. These results show that the model can be used to effectively predict the results of PCR. This is the first paper to optimize primer design by screening the candidate primer set with a neural network model.
Wireless Charger Network (WCN) emerges as a promising networking paradigm, employing wireless chargers with Wireless Power Transfer (WPT) technology to provide long-term and sustainable energy supply for future networ...
详细信息
Speech enhancement is a demanding task in automated speech processing pipelines, focusing on separating clean speech from noisy channels. Transformer based models have recently bested RNN and CNN models in speech enha...
详细信息
Federated learning (FL) has gained increasing popularity in today’s privacy-focused world due to its ability to break data silos. Although federated learning can keep all the training data private at each site, it re...
详细信息
ISBN:
(数字)9798331506209
ISBN:
(纸本)9798331506216
Federated learning (FL) has gained increasing popularity in today’s privacy-focused world due to its ability to break data silos. Although federated learning can keep all the training data private at each site, it results in clients not being effectively monitored, which may pose security risks to the learning process. Previous studies mainly focus on backdoor and poisoning attacks against federated learning. In this paper, we propose a new stegomalware attack in FL, where an attacker, disguised as a benign client, hides malware into his local model and distributes the malware to other clients through the FL process. Conducting such an attack faces two challenges. First, embedding data into a local model may degrade its model performance, making it easier for a server in FL to detect this anomaly. Second, the local model with embedded malware is recalculated after model aggregation, which unavoidably alters the malware’s carrier and hinders the success of malware extraction. To address these challenges, we propose a method called StegoFL to incorporate steganography into federated learning to transmit malware. Specifically, we split the malware to be transmitted into segments and randomly select a few model parameters as carriers. Each malware segment is concealed within these carriers over several training rounds. We also propose a simple value-mapping method to extract the embedded data by comparing the aggregated carrier values with a threshold. Experimental results demonstrate that StegoFL can circumvent server-side detection mechanisms, i.e., accuracy tests and parametric distribution comparisons. In addition, it increases transmission capacity by at least 50 times compared to state-of-the-art covert communication methods in federated learning.
As one kind of distributed machine learning technique, federated learning enables multiple clients to build a model across decentralized data collaboratively without explicitly aggregating the data. Due to its ability...
详细信息
Accurate segmentation of the ventricles from cardiac magnetic resonance images (CMRIs) is crucial for enhancing the diagnosis and analysis of heart conditions. Deep learning-based segmentation methods have recently ga...
详细信息
English has emerged as the predominant global language, making it a central focus in educational field seeking to integrate English with other disciplines for improved learning efficiency. Despite numerous studies dem...
详细信息
We revisit and adapt the extended sequential quadratic method (ESQM) in [3] for solving a class of difference-of-convex optimization problems whose constraints are defined as the intersection of level sets of Lipschit...
详细信息
暂无评论