3D-Organoid-SwinNet: High-content profiling of 3D organoids.

Profiling of Patient-Derived organoids is necessary for drug screening and precision medicine. This step requires accurate segmentation of three-dimensional cellular structures followed by protein readouts. While fully Convolutional Neural Networks are widely used in medical image segmentation, they struggle to capture long-range dependencies necessary for accurate segmentation.

Self-supervised region-aware segmentation of COVID-19 CT images using 3D GAN and contrastive learning.

Medical image segmentation is a key initial step in several therapeutic applications. While most of the automatic segmentation models are supervised, which require a well-annotated paired dataset, we introduce a novel annotation-free pipeline to perform segmentation of COVID-19 CT images. Our pipeline consists of three main subtasks: automatically generating a 3D pseudo-mask in self-supervised mode using a generative adversarial network (GAN), leveraging the quality of the pseudo-mask, and building a multi-objective segmentation model to predict lesions.

The impact of recency and adequacy of historical information on sepsis predictions using machine learning.

Sepsis is a major public and global health concern. Every hour of delay in detecting sepsis significantly increases the risk of death, highlighting the importance of accurately predicting sepsis in a timely manner. A growing body of literature has examined developing new or improving the existing machine learning (ML) approaches for timely and accurate predictions of sepsis.