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Schizophrenia is a debilitating psychiatric disorder that can significantly affect a patient's quality of life and lead to permanent brain damage. Although medical research has identified certain genetic risk factors, the specific pathogenesis of the disorder remains unclear. Despite the prevalence of research employing magnetic resonance imaging, few studies have focused on the gene level and gene expression profile involving a large number of screened genes. However, the high dimensionality of genetic data presents a great challenge to accurately modeling the data. To tackle the current challenges, this study presents a novel feature selection strategy that utilizes heuristic feature fusion and a multi-objective optimization genetic algorithm. The goal is to improve classification performance and identify the key gene subset for schizophrenia diagnostics. Traditional gene screening techniques are inadequate for accurately determining the precise number of key genes associated with schizophrenia. Our innovative approach integrates a filter-based feature selection method to reduce data dimensionality and a multi-objective optimization genetic algorithm for improved classification tasks. By combining the filtering and wrapper methods, our strategy leverages their respective strengths in a deliberate manner, leading to superior classification accuracy and a more efficient selection of relevant genes. This approach has demonstrated significant improvements in classification results across 11 out of 14 relevant datasets. The performance on the remaining three datasets is comparable to the existing methods. Furthermore, visual and enrichment analyses have confirmed the practicality of our proposed method as a promising tool for the early detection of schizophrenia. [ABSTRACT FROM AUTHOR]

Accurate medical image segmentation can effectively assist disease diagnosis and treatment. While neural networks were often applied to solve the segmentation problem in recent computer-aided diagnosis, the metadata of patients was usually neglected. Motivated by this, we propose a medical image segmentation and diagnosis framework that takes advantage of both the image and the patient’s metadata, such as gender and age. We present MMY-NET: a new multi-modal network for simultaneous tumor segmentation and diagnosis exploiting patient metadata. Our architecture consists of three parts: a visual encoder, a text encoder, and a decoder with a self-attention block. Specifically, we design a text preprocessing block to embed metadata effectively, and the image features and text embedding features are then fused on several layers between the two encoders. Moreover, Interlaced Sparse Self-Attention is added to the decoder to further boost the performance. We apply our algorithm on 1 private dataset (ZJU2), and 1 private dataset (LISHUI) for zero-shot validation. Results show that our algorithm combined with metadata outperforms its counterpart without metadata by a large margin for basal cell carcinoma segmentation (14.3 % improvement of IoU and 8.5 % of Dice on the ZJU2 dataset, and 7.1 % IoU on the LIZHUI validation dataset). Additionally, we applied MMY-Net to 1 public segmentation dataset to demonstrate its general segmentation capability. MMY-Net outperforms the state-of-the-art methods on the GlaS dataset. [ABSTRACT FROM AUTHOR]

With the increasing complexity of autonomous driving tasks, the computational demands on single vehicular computing units have escalated, more and more tasks need to be offloaded to the edge. These tasks vary in latency sensitivity: real-time tasks, critical for passenger safety, require strict deadline adherence, whereas the latency of standard tasks mainly affects the user experience and has more flexible constraints. Addressing the challenge of selecting suitable edge computing nodes to enhance the offloading success rate of real-time tasks amidst a vast and heterogeneous cluster becomes crucial. This paper introduces the adaptive priority-based hierarchical task offloading (APHTO) algorithm, which optimizes task offloading strategies by accounting for the diverse latency constraints of different task types. Experiments demonstrate that under optimal performance conditions, APHTO significantly outperforms existing algorithms such as Min–Min, Max–Min, CUS, and FMS in reducing task latency by 20.31%, increasing offloading success rates by 35.83%, and improving resource utilization by 30.21%, marking a substantial advancement in task offloading strategies for autonomous driving integrated with MEC. [ABSTRACT FROM AUTHOR]

The thickness of the choroid is considered to be an important indicator of clinical diagnosis. Therefore, accurate choroid segmentation in retinal OCT images is crucial for monitoring various ophthalmic diseases. However, this is still challenging due to the blurry boundaries and interference from other lesions. To address these issues, we propose a novel prior-guided and knowledge diffusive network (PGKD-Net) to fully utilize retinal structural information to highlight choroidal region features and boost segmentation performance. Specifically, it is composed of two parts: a Prior-mask Guided Network (PG-Net) for coarse segmentation and a Knowledge Diffusive Network (KD-Net) for fine segmentation. In addition, we design two novel feature enhancement modules, Multi-Scale Context Aggregation (MSCA) and Multi-Level Feature Fusion (MLFF). The MSCA module captures the long-distance dependencies between features from different receptive fields and improves the model's ability to learn global context. The MLFF module integrates the cascaded context knowledge learned from PG-Net to benefit fine-level segmentation. Comprehensive experiments are conducted to evaluate the performance of the proposed PGKD-Net. Experimental results show that our proposed method achieves superior segmentation accuracy over other state-of-the-art methods. Our code is made up publicly available at: https://github.com/yzh-hdu/choroid-segmentation. • Designed a multi-scale context aggregation module to capture non-local information. • Propose a multi-level feature fusion module to boost segmentation performance. • Conduct sufficient experiments and the state-of-the-art performance is achieved. [ABSTRACT FROM AUTHOR]