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Ruiz Ankersen posted an update 6 months ago
An essential capability of artificial intelligence systems is the constant ability to grasp and retain innovative concepts without losing track of previously acquired knowledge. Yet, even the most advanced deep learning networks often demonstrate a rapid decay of previously learned knowledge following training with new datasets. Lifelong dual generative adversarial networks (LD-GANs) are structured using two generative adversarial networks (GANs), a Teacher and an Assistant, which are engaged in a continuous learning and teaching process of multiple tasks. For the dual GANs, a single discriminator is responsible for determining the realism of the generated images. The proposed lifelong self-knowledge distillation (LSKD) algorithm, designed for lifelong learning (LLL), trains LD-GANs by learning each new task during the process. In an adversarial game setting, LSKD empowers knowledge transfer from a more knowledgeable player to another, intertwining it with learning from a newly introduced dataset. Compared to other LLM models, LD-GANs are marked by memory efficiency, and they do not necessitate parameter freezing after each learning instance of a given task. Likewise, LD-GANs are elevated to the Teacher module within a Teacher-Student network, with the intent of harmonizing data representations across multiple domains during the language learning phase. Unsupervised lifelong representation learning shows a clear advantage for the proposed framework, exceeding the performance of other methods in experimental trials.
Digital healthcare services have become fundamentally embedded within our daily existence. A considerable increase is evident in the application of medical wearables by healthcare practitioners and patients for diagnosis and treatment, leading to a significantly improved diagnostic and therapeutic outcome. Nevertheless, the improper utilization of medical records could lead to the revelation of sensitive patient data. To safeguard patient privacy in medical wearable technology, we present a novel blockchain-based data access security system. Using elliptic curve cryptography and zero-knowledge authentication, the blockchain platform verifies the identities of patients and doctors. Furthermore, a deep reinforcement learning-based smart recommendation technique is developed for suggesting suitable doctors to patients. Subsequently, patients authorize designated physicians to gain access to their medical records, and specialized smart contracts designed for the secure handling of medical wearable data will manage any subsequent access requests. Security analysis and experimental findings validate the effectiveness of the proposed scheme in protecting patient privacy during treatment by means of secure authentication and access management for medical wearables.
Coronary heart disease and hypertension are more frequent causes of cardiovascular conditions than pulmonary arterial hypertension (PAH). The diagnosis of PAH heavily depends on a thorough evaluation of CT scans, along with other relevant medical imaging procedures. Deep learning has significantly enhanced the field of medical image processing concerning medical imagery. However, the data’s connection to patient privacy demands that medical institutions, as its custodians, diligently protect the security of their patients’ private information. This situation compels medical institutions to ponder the complexities of creating data-driven, deep learning-reinforced medical diagnostic methodologies. Big Data architecture is necessary for high-quality data to fuel deep learning; however, simultaneously, strong measures are needed to protect patient privacy and prevent data leakage incidents. In light of the preceding obstacles, a hierarchical hybrid automatic segmentation model for pulmonary blood vessels is developed, utilizing local and federated learning strategies to segment the blood vessels effectively. The experimental process confirms the proposal’s ability to automatically extract vessels from the initial CT image. The model, which utilizes federated learning, showcases exceptional performance, in alignment with preserving Big Data privacy.
The increasing sophistication of artificial intelligence technologies has resulted in a significant adoption of deep learning techniques within biomedical data analytics and digital healthcare environments. Although AI-powered diagnostic methods are emerging, they still fall short of meeting the complete healthcare needs. Significant clinical value in diagnosing adverse perinatal results is associated with middle cerebral artery (MCA) hemodynamic parameters. Although this may be the case, the present method of sonographic measurement continues to be a time-consuming and cumbersome task. To minimize the workload of sonographers, MCAS-GP, a deep learning framework, is put forward to accomplish the segmentation of the Middle Cerebral Artery and the determination of its gate. MCAS-GP, used in fetal MCA Doppler assessments, provides automatic segmentation of the MCA and the detection of its corresponding gate location. Within the MCAS-GP framework, a novel learnable atrous spatial pyramid pooling (LASPP) module is developed for the adaptive learning of multi-scale features. We propose the Affiliation Index, a novel evaluation metric, to assess the effectiveness of the output gate’s strategic placement in the system. To gauge the effectiveness of our MCAS-GP proposal, a large-scale MCA dataset was assembled in collaboration with the International Peace Maternity and Child Health Hospital of China’s welfare institute (IPMCH). A thorough evaluation of MCAS-GP using the MCA dataset, along with two other publicly available surgical datasets, reveals notable advancements in both accuracy and prediction speed.
Knee hyperextension, clinically referred to as genu recurvatum, displays a complex gait profile with diverse contributing factors and is frequently accompanied by symptoms of knee weakness, motor control dysfunction, and the presence of spasticity. To avert additional harm to the knee joint, early intervention or preventative measures for knee hyperextension, arising from atypical forces on the soft tissues, are beneficial. A knee exoskeleton’s potential to diminish hyperextension and augment swing range of motion was assessed in five children and adolescents with unilateral genu recurvatum in this study. mirna inhibitor The exoskeleton, used by each participant in three visits, provided torque assistance during both stance and swing phases of their walking, controlled by an impedance control law. In the concluding phase of validation testing, the exoskeleton proved effective in mitigating knee hyperextension, resulting in a reduction of average peak knee extension from 2.47 degrees without the exoskeleton to a range of 0.99-1.03 degrees with the exoskeleton. Concurrently, the exoskeleton enhanced swing range of motion by an average of 140.45 degrees. However, the exoskeleton’s success in normalizing the kinematic characteristics did not translate into better spatio-temporal asymmetry measurements. Through this work, a promising application of a robotic knee exoskeleton for the improvement of kinematic characteristics in genu recurvatum gait is presented.
Data-driven methods are employed to engineer stimuli, including electrical currents, to elicit the desired neural responses in distinct types of neurons, thus contributing to the treatment of neural disorders.
The problem of stimulus design is presented as the calculation of the inverse of a non-linear, many-to-one mapping. This transformation accepts waveform parameters as inputs, and yields the corresponding neural output, which is determined solely from experimental data. For the purpose of estimating the previously mentioned inverse mapping, a novel optimization framework, PATHFINDER, is introduced. Different dataset sizes are used in toy and complex biological neuron models to perform a comparison of data-driven methods, particularly focusing on conditional density estimation and numerically inverting an estimated forward mapping.
Based on toy data and computational models of biological neurons, PATHFINDER demonstrates superior performance to existing methods when faced with limited sample sizes, specifically fewer than a few hundred examples.
Historically, the creation of these stimuli has relied on pre-existing models and/or rudimentary intuitions, frequently supplemented by iterative experimentation. The inherent difficulties in accurately modeling neural responses are compounded by the subtle but significant effects of stimuli on neural membrane potentials. Consequently, data-driven methods represent a compelling alternative. Our findings indicate that PATHFINDER’s suitability for optimizing stimulation parameters in neural disorder experiments and treatments is enhanced by its low data point requirement.
Historically, the design of these prompts has been based on pre-defined models and/or elementary intuitions, sometimes supplemented by trial and error. Accurate modeling of neuronal responses presents inherent challenges, coupled with the multifaceted influence of stimuli on neuronal membrane potentials, thereby suggesting data-driven methods as an attractive alternative. Experiments using PATHFINDER suggest its potential for optimizing stimulation parameters in neural disorder treatments and research, due to the minimal amount of data needed.
A standard diagnostic technique, electrocardiography (ECG), comprehensively evaluates the heart’s electrical activity, proving vital in recognizing many cardiovascular diseases. Deep neural networks have been extensively investigated for classifying ECG recordings, and have yielded significant improvements as reported in the scholarly literature. While this performance is contingent upon centralized training data, a situation frequently not encountered in actual applications, where data is distributed across various sites and only a portion is labeled. Therefore, our work develops an ECG classification system that prioritizes data security and overall system effectiveness. We investigated the computational complexity of existing deep learning models, ultimately determining that temporal convolutional network (TCN) models possessed the greatest efficiency. Utilizing TCN models as a foundation, a modified split-learning (SL) system was constructed. This system matches the classification performance of the basic SL while diminishing server-client communication overhead by 717% and lowering client-side computational costs by 465%, in comparison to the original SL system derived from the TCN network.
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