Deep learning approaches, though effective in enhancing medical imagery, are hampered by the presence of low-quality training datasets and an insufficient supply of corresponding training samples. In this paper, a Siamese structure-based method (SSP-Net) is proposed for enhancing dual-input images. This approach focuses on the texture enhancement of target highlights and the consistent background contrast, leveraging unpaired low-quality and high-quality medical images. check details The proposed method, in addition, incorporates the generative adversarial network mechanism, achieving structure-preserving enhancement through iterative adversarial learning processes. medical philosophy A comparative analysis of the proposed SSP-Net with existing state-of-the-art methods, using extensive experimental trials, reveals its superior performance in unpaired image enhancement.
Depression, characterized by prolonged low mood and decreased interest in activities, is a mental disorder that substantially hinders daily functioning. Distress may arise from a confluence of psychological, biological, and social influences. Major depression or major depressive disorder, more severe forms of depression, are characterized as clinical depression. Electroencephalography and speech signal analysis have been increasingly applied to early depression diagnosis; nonetheless, their current applicability is predominantly limited to situations of moderate or severe depression. In order to boost diagnostic precision, we've integrated audio spectrograms and multiple EEG frequency channels. In order to achieve this, we combined diverse levels of spoken language and EEG data to produce rich descriptive characteristics, and then used vision transformers and a variety of pre-trained networks to analyze the speech and EEG signals. The Multimodal Open Dataset for Mental-disorder Analysis (MODMA) dataset was instrumental in our extensive experiments, resulting in a significant improvement in diagnosing mild depression, evidenced by high precision (0.972), recall (0.973), and F1-score (0.973). Finally, in support of the project, a web application was developed using Flask, with the source code readily available at https://github.com/RespectKnowledge/EEG. Speech, a significant component of depression, encompassing MultiDL.
Although significant advances have been made in graph representation learning, the practical implications of continual learning, involving the continuous arrival of new node types (such as new research areas in citation networks or fresh product types in co-purchasing networks) and their related connections, thereby causing catastrophic forgetting of previously learned categories, has been understudied. Existing methodologies either neglect the intricate topological structure or trade off plasticity for robustness. To achieve this, we introduce Hierarchical Prototype Networks (HPNs), which extract various levels of abstract knowledge in the form of prototypes to represent the ever-growing graphs. We first apply a series of Atomic Feature Extractors (AFEs) to encode the elemental attributes of the target node and its topological structure. We then create HPNs to ensure the adaptive selection of fitting AFEs, where each node incorporates three prototype levels. Adding a new node type will selectively activate and refine the corresponding AFEs and prototypes at each level, ensuring that other components of the system remain stable to guarantee overall performance with respect to current nodes. Hypothetically, our initial demonstration reveals a bounded memory usage for HPNs, irrespective of the quantity of tasks encountered. We then show how, under reasonable conditions, learning new tasks won't change the prototypes linked to past data, preventing the occurrence of forgetting. The superiority of HPNs, as predicted theoretically, is validated by experiments conducted on five datasets, exceeding the performance of state-of-the-art baselines and demonstrating significantly reduced memory consumption. To access the code and datasets for HPNs, please navigate to the following link: https://github.com/QueuQ/HPNs.
Due to their capacity to extract meaningful latent representations, variational autoencoders (VAEs) are commonly used for unsupervised text generation; however, this technique often relies on an isotropic Gaussian distribution, which may not adequately represent the true distribution of texts. In practical applications, sentences carrying different semantic information may not follow the simple isotropic Gaussian distribution. Due to the dissimilarity of subject matter found within the texts, their distribution is almost certainly more convoluted and diverse. In view of this, we propose a flow-enhanced Variational Autoencoder for topic-oriented language modelling (FET-LM). Separate topic and sequence latent variable modeling is employed by the FET-LM model, which incorporates a normalized flow of householder transformations for the sequence posterior. This technique allows for a more precise representation of complex text distributions. FET-LM, with learned sequence knowledge as a foundation, further benefits from a neural latent topic component. This reduces the workload of unsupervised topic learning and effectively guides the sequence component to collect and consolidate topic data during training. To ensure greater thematic coherence in the generated texts, we further incorporate the topic encoder as a discriminatory element. The FET-LM's noteworthy performance on abundant automatic metrics and across three generation tasks showcases not only its comprehension of interpretable sequence and topic representations, but also its ability to produce semantically sound, high-quality paragraphs.
Advocating for the acceleration of deep neural networks, filter pruning offers a solution that does not necessitate dedicated hardware or libraries, while maintaining high levels of prediction accuracy. Pruning, often cast as a variant of l1-regularized training, presents two difficulties: (1) the l1-norm's non-scaling invariance, whereby the penalty is dependent on the magnitude of the weights, and (2) the need for a robust method to choose the penalty coefficient, which must balance high pruning ratios with minimal accuracy reduction. In order to resolve these concerns, we present a lightweight pruning technique, termed adaptive sensitivity-based pruning (ASTER), which 1) preserves the scale-invariance of unpruned filter weights and 2) adjusts the pruning threshold dynamically throughout the training process. Aster's on-the-fly computation of the loss's sensitivity to the threshold bypasses retraining, and this is implemented with high efficiency using L-BFGS only on the batch normalization (BN) layers. Subsequently, it modifies the threshold to uphold a precise balance between the percentage of pruned elements and the model's functionality. We have carried out extensive tests on a range of top-tier CNN models with benchmark datasets, showcasing our approach's ability to reduce FLOPs while preserving accuracy. On the ILSVRC-2012 dataset, our technique yielded a reduction of over 76% in FLOPs for ResNet-50, while experiencing only a 20% decrease in Top-1 accuracy. In contrast, a substantial 466% decrease in FLOPs was observed with the MobileNet v2 model. The decline was limited to a 277% decrease. Even a lightweight MobileNet v3-small classification model benefits from a significant 161% reduction in floating-point operations (FLOPs) with ASTER, resulting in only a minimal 0.03% drop in Top-1 accuracy.
The significance of deep learning in diagnostic healthcare is undeniable and growing. The key to superior diagnostic accuracy lies in the meticulous design of deep neural networks (DNNs). Despite their demonstrated success in image analysis, supervised deep neural networks constructed using convolutional layers are often constrained in their feature exploration ability, which originates from the restricted receptive field and biased feature extraction within conventional convolutional neural networks (CNNs), leading to compromised network performance. This study presents a novel feature exploration network, the manifold embedded multilayer perceptron (MLP) mixer, or ME-Mixer, which combines supervised and unsupervised features for disease diagnostics. The proposed approach leverages a manifold embedding network for extracting class-discriminative features, followed by the application of two MLP-Mixer-based feature projectors for encoding the features within the context of the global reception field. Adding our ME-Mixer network as a plugin is a straightforward way to enhance any existing CNN, given its generalized nature. A comprehensive assessment of two medical datasets is undertaken. The classification accuracy is significantly improved by their method, compared to various DNN configurations, while maintaining acceptable computational complexity, as the results demonstrate.
Modern objective diagnostics is shifting focus from blood and urine analysis to less invasive dermal interstitial fluid health monitoring. However, the stratum corneum, the outermost layer of skin, presents a significant obstacle to the uncomplicated access of the fluid, precluding the use of non-invasive methods, and necessitates the use of invasive, needle-based technology. Simple, minimally invasive approaches are essential for clearing this hurdle.
A strategy for handling this difficulty involved the creation and testing of a flexible patch, resembling a Band-Aid, for interstitial fluid sampling. This patch utilizes simple resistive heating elements to thermally perforate the stratum corneum, allowing the release of fluids from underlying skin tissue without applying any external pressure. Biolog phenotypic profiling Hydrophilic microfluidic channels, autonomously operated, transport fluid to an on-patch reservoir.
Experimental data from living, ex-vivo human skin models confirmed the device's ability to rapidly gather adequate interstitial fluid required for biomarker quantification. Finite element modeling findings highlighted that the patch can pass through the stratum corneum without causing the skin temperature to rise to levels that stimulate pain receptors in the dermis containing numerous nerves.
Utilizing only straightforward, commercially viable manufacturing methods, this patch collects human bodily fluids at a rate exceeding that of various microneedle-based patches, painlessly and without any physical penetration