Mamba-fusion for privacy-preserving disease prediction.

Accurate disease prediction is essential for improving patient outcomes. Privacy regulations like GDPR and HIPAA limit data sharing, hindering the development of robust predictive models across institutions. FL and multi-modal fusion frameworks counter these problems but are restricted in scalability, inter-client communication, and heterogeneity of data modalities.

Spectral feature modeling with graph signal processing for brain connectivity in autism spectrum disorder.

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition associated with disrupted brain connectivity. Traditional graph-theoretical approaches have been widely employed to study ASD biomarkers; however, these methods are often limited to static topological measures and lack the capacity to capture spectral characteristics of brain activity, especially in multimodal data settings. This limits their ability to model dynamic neural interactions and reduces their diagnostic precision.

A Feature Fusion Attention-based Deep Learning Algorithm for Mammographic Architectural Distortion Classification.

Architectural Distortion (AD) is a common abnormality in digital mammograms, alongside masses and microcalcifications. Detecting AD in dense breast tissue is particularly challenging due to its heterogeneous asymmetries and subtle presentation. Factors such as location, size, shape, texture, and variability in patterns contribute to reduced sensitivity.

Correction: Jabbar et al. Transfer Learning-Based Model for Diabetic Retinopathy Diagnosis Using Retinal Images. 2022, , 535.

In the original publication [...

Transfer Learning-Based Model for Diabetic Retinopathy Diagnosis Using Retinal Images.

Diabetic retinopathy (DR) is a visual obstacle caused by diabetic disease, which forms because of long-standing diabetes mellitus, which damages the retinal blood vessels. This disease is considered one of the principal causes of sightlessness and accounts for more than 158 million cases all over the world. Since early detection and classification could diminish the visual impairment, it is significant to develop an automated DR diagnosis method.