Parallel Multi-Circuit Quantum Feature Fusion in Hybrid Quantum-Classical Convolutional Neural Networks for Breast Tumor Classification

A novel deep hybrid segmentation framework named Residual-SwinCA-Net has been proposed for malignant lesion segmentation in breast ultrasound images, utilizing a combination of residual CNN modules and customized Swin Transformer blocks to enhance feature extraction and gradient stability. The framework also incorporates advanced techniques for noise suppression and boundary preservation to improve segmentation accuracy.

A recent study published on arXiv introduces a distribution-based framework aimed at mitigating individual skin tone bias in skin lesion classification, emphasizing the importance of treating skin tone as a continuous attribute. The research employs kernel density estimation to model skin tone distributions and proposes a distance-based reweighting loss function to address underrepresentation of minority tones.

PRISM has been introduced as a lightweight fully convolutional classifier for multivariate time series classification, utilizing symmetric multi-resolution convolutional layers to efficiently capture both short-term patterns and longer-range dependencies. This model significantly reduces the number of learnable parameters while maintaining performance across various benchmarks, including human activity recognition and sleep state detection.

A novel approach to Granger Causality (GC) using deep neural networks (DNNs) has been proposed, focusing on the joint modeling of multivariate time series data. This method aims to enhance the understanding of complex associations that traditional vector autoregressive models struggle to capture, particularly in non-linear contexts.

A novel segmentation framework called Neural Tissue Relation Modeling (NTRM) has been introduced to enhance histopathology image segmentation in non-melanoma skin cancer diagnostics. This framework integrates a tissue-level graph neural network with convolutional neural networks (CNNs) to better model spatial and functional relationships among tissue types, addressing challenges in regions with overlapping or morphologically similar tissues.

A new study introduces JaGuard, a receiver-centric temporal graph neural network designed to mitigate jamming in Global Navigation Satellite Systems (GNSS). The system employs a dynamic graph regression approach to estimate and correct positioning errors caused by intentional interference, utilizing a heterogeneous star graph representation of satellite-receiver interactions.

A new study introduces a robust version of the Variational Model Based Tailored UNet (VM_TUNet), which integrates variational methods with deep learning to enhance image segmentation, particularly in noisy images with blurred boundaries. The framework employs an edge detector and a mean curvature term within a modified Cahn-Hilliard equation, demonstrating improved performance through two collaborative modules for efficient preprocessing and stable local computations.

A novel hybrid framework has been introduced for precise liver tumor segmentation in CT scans, combining an attention-enhanced U-Net with handcrafted radiomics and voxel-wise 3D CNN refinement. This approach aims to improve the accuracy and efficiency of tumor delineation, addressing challenges such as low contrast and blurred boundaries in imaging.