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. Our proposed 3D GAN architecture removes infected regions from COVID-19 images and generates synthesized healthy images while keeping the 3D structure of the lung the same. Then, a 3D pseudo-mask is generated by subtracting the synthesized healthy images from the original COVID-19 CT images. We enhanced pseudo-masks using a contrastive learning approach to build a region-aware segmentation model to focus more on the infected area. The final segmentation model can be used to predict lesions in COVID-19 CT images without any manual annotation at the pixel level. We show that our approach outperforms the existing state-of-the-art unsupervised and weakly-supervised segmentation techniques on three datasets by a reasonable margin. Specifically, our method improves the segmentation results for the CT images with low infection by increasing sensitivity by 20% and the dice score up to 4%. The proposed pipeline overcomes some of the major limitations of existing unsupervised segmentation approaches and opens up a novel horizon for different applications of medical image segmentation.

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. This study contributes to this literature by providing clear insights regarding the role of the recency and adequacy of historical information in predicting sepsis using ML. To this end, we implemented a deep learning model using a bidirectional long short-term memory (BiLSTM) algorithm and compared it with six other ML algorithms based on numerous combinations of the prediction horizons (to capture information recency) and observation windows (to capture information adequacy) using different measures of predictive performance. Our results indicated that the BiLSTM algorithm outperforms all other ML algorithms and provides a great separability of the predicted risk of sepsis among septic versus non-septic patients. Moreover, decreasing the prediction horizon (in favor of information recency) always boosts the predictive performance; however, the impact of expanding the observation window (in favor of information adequacy) depends on the prediction horizon and the purpose of prediction. More specifically, when the prediction is responsive to the positive label (i.e., Sepsis), increasing historical data improves the predictive performance when the prediction horizon is short-moderate.