Ensemble learning driven Kolmogorov-Arnold Networks-based Lung Cancer classification.

Early Lung Cancer (LC) detection is essential for reducing the global mortality rate. The limitations of traditional diagnostic techniques cause challenges in identifying LC using medical imaging data. In this study, we aim to develop a robust LC detection model.

The effects of young and aged, male and female megakaryocyte conditioned media on angiogenic properties of endothelial cells.

With aging, the risk of fractures and compromised healing increases. Angiogenesis plays a significant role in bone healing and is impaired with aging. We have previously shown the impact of megakaryocytes (MKs) in regulating bone healing.

Radio-pathomic estimates of cellular growth kinetics predict survival in recurrent glioblastoma.

A radio-pathomic machine learning (ML) model has been developed to estimate tumor cell density, cytoplasm density (Cyt) and extracellular fluid density (ECF) from multimodal MR images and autopsy pathology. In this multicenter study, we implemented this model to test its ability to predict survival in patients with recurrent glioblastoma (rGBM) treated with chemotherapy. Pre- and post-contrast T-weighted, FLAIR and ADC images were used to generate radio-pathomic maps for 51 patients with longitudinal pre- and post-treatment scans.

A Feature-Fusion Technique-Based Alzheimer's Disease Classification Using Magnetic Resonance Imaging.

Background: Early identification of Alzheimer's disease (AD) is essential for optimal treatment and management. Deep learning (DL) technologies, including convolutional neural networks (CNNs) and vision transformers (ViTs) can provide promising outcomes in AD diagnosis. However, these technologies lack model interpretability and demand substantial computational resources, causing challenges in the resource-constrained environment.