Mortality Prediction of Pulmonary Embolism Patients with Deep Learning and XGBoost

Pulmonary Embolism (PE) is a serious cardiovascular condition that remains a leading cause of mortality and critical illness, underscoring the need for enhanced diagnostic strategies. Conventional clinical methods have limited success in predicting 30-day in-hospital mortality of PE patients. In this study, we present a new algorithm, called PEP-Net, for 30-day mortality prediction of PE patients based on the initial imaging data (CT) that opportunistically integrates a 3D Residual Network (3DResNet) with Extreme Gradient Boosting (XGBoost) algorithm with patient level binary labels without annotations of the emboli and its extent. Our proposed system offers a comprehensive prediction strategy by handling class imbalance problems, reducing overfitting via regularization, and reducing the prediction variance for more stable predictions. PEP-Net was tested in a cohort of 193 volumetric CT scans diagnosed with Acute PE, and it demonstrated a superior performance by significantly outperforming baseline models (76-78\%) with an accuracy of 94.5\% (+/-0.3) and 94.0\% (+/-0.7) when the input image is either lung region (Lung-ROI) or heart region (Cardiac-ROI). Our results advance PE prognostics by using only initial imaging data, setting a new benchmark in the field. While purely deep learning models have become the go-to for many medical classification (diagnostic) tasks, combined ResNet and XGBoost models herein outperform sole deep learning models due to a potential reason for having lack of enough data.

Selecting the Best Optimizers for Deep Learning based Medical Image Segmentation

The goal of this work is to identify the best optimizers for deep learning in the context of cardiac image segmentation and to provide guidance on how to design segmentation networks with effective optimization strategies. Adaptive learning helps with fast convergence by starting with a larger learning rate (LR) and gradually decreasing it. Momentum optimizers are particularly effective at quickly optimizing neural networks within the accelerated schemes category. By revealing the potential interplay between these two types of algorithms (LR and momentum optimizers or momentum rate (MR) in short), in this article, we explore the two variants of SGD algorithms in a single setting. We suggest using cyclic learning as the base optimizer and integrating optimal values of learning rate and momentum rate. We investigated the relationship of LR and MR under an important problem of medical image segmentation of cardiac structures from MRI and CT scans. We conducted experiments using the cardiac imaging dataset from the ACDC challenge of MICCAI 2017, and four different architectures shown to be successful for cardiac image segmentation problems. Our comprehensive evaluations demonstrated that the proposed optimizer achieved better results (over a 2\% improvement in the dice metric) than other optimizers in deep learning literature with similar or lower computational cost in both single and multi-object segmentation settings. We hypothesized that combination of accelerated and adaptive optimization methods can have a drastic effect in medical image segmentation performances. To this end, we proposed a new cyclic optimization method (\textit{CLMR}) to address the efficiency and accuracy problems in deep learning based medical image segmentation. The proposed strategy yielded better generalization in comparison to adaptive optimizers.