Autoencoder imputation of missing heterogeneous data for Alzheimer's disease classification.

Missing Alzheimer's disease (AD) data is prevalent and poses significant challenges for AD diagnosis. Previous studies have explored various data imputation approaches on AD data, but the systematic evaluation of deep learning algorithms for imputing heterogeneous and comprehensive AD data is limited. This study investigates the efficacy of denoising autoencoder-based imputation of missing key features of heterogeneous data that comprised tau-PET, MRI, cognitive and functional assessments, genotype, sociodemographic, and medical history.

Mixed Neuroendocrine Non-Neuroendocrine Tumor (MINEN) of the Liver: Report of Two Cases and Review of the Literature.

Objective: To highlight two cases mixed neuroendocrine non-neuroendocrine tumors (MINEN) of the liver and to review the literature till date. To present two cases of MINEN of the liver diagnosed in our centre with clinical & diagnostic workup, the treatment modalities, and follow up. Extensive review of the literature and compilation of the presentation and treatment modalities used in those cases.