Residual-SwinCA-Net: A Channel-Aware Integrated Residual CNN-Swin Transformer for Malignant Lesion Segmentation in BUSI

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 new framework named RF-Deep has been developed to enhance out-of-distribution detection in lung cancer segmentation, utilizing a random forest classifier that leverages deep features from a pretrained transformer encoder. This innovation addresses the challenges faced by existing models when applied to out-of-distribution datasets, ensuring more reliable segmentation of lung cancers from CT scans.

A new study proposes a structured initialization method for Vision Transformers (ViTs), aiming to integrate the strong inductive biases of Convolutional Neural Networks (CNNs) without altering the architecture. This approach is designed to enhance performance on small datasets while maintaining scalability as data increases.

The Iwin Transformer has been introduced as a novel hierarchical vision transformer that operates without position embeddings, utilizing interleaved window attention and depthwise separable convolution to enhance performance across various visual tasks. This architecture allows for direct fine-tuning from low to high resolution, achieving notable results such as 87.4% top-1 accuracy on ImageNet-1K.

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 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.