CT-Mamba: A Hybrid Convolutional State Space Model for Low-Dose CT Denoising

Low-dose CT (LDCT) significantly reduces the radiation dose received by patients, thereby decreasing potential health risks. However, dose reduction introduces additional noise and artifacts, adversely affecting image quality and clinical diagnosis. Currently, denoising methods based on convolutional neural networks (CNNs) face limitations in long-range modeling capabilities, while Transformer-based denoising methods, although capable of powerful long-range modeling, suffer from high computational complexity. Furthermore, the denoised images predicted by deep learning-based techniques inevitably exhibit differences in noise distribution compared to Normal-dose CT (NDCT) images, which can also impact the final image quality and diagnostic outcomes. In recent years, the feasibility of applying deep learning methods to low-dose CT imaging has been demonstrated, leading to significant achievements. This paper proposes CT-Mamba, a hybrid convolutional State Space Model for LDCT image denoising. The model combines the local feature extraction advantages of CNNs with Mamba's global modeling capability, enabling it to capture both local details and global context. Additionally, a Mamba-driven deep noise power spectrum (NPS) loss function was designed to guide model training, ensuring that the noise texture of the denoised LDCT images closely resembles that of NDCT images, thereby enhancing overall image quality and diagnostic value. Experimental results have demonstrated that CT-Mamba performs excellently in reducing noise in LDCT images, enhancing detail preservation, and optimizing noise texture distribution, while demonstrating statistically similar radiomics features to those of NDCT images (p > 0.05). The proposed CT-Mamba demonstrates outstanding performance in LDCT denoising and holds promise as a representative approach for applying the Mamba framework to LDCT denoising tasks.

Building an Automated and Self-Aware Anomaly Detection System

Organizations rely heavily on time series metrics to measure and model key aspects of operational and business performance. The ability to reliably detect issues with these metrics is imperative to identifying early indicators of major problems before they become pervasive. It can be very challenging to proactively monitor a large number of diverse and constantly changing time series for anomalies, so there are often gaps in monitoring coverage, disabled or ignored monitors due to false positive alarms, and teams resorting to manual inspection of charts to catch problems. Traditionally, variations in the data generation processes and patterns have required strong modeling expertise to create models that accurately flag anomalies. In this paper, we describe an anomaly detection system that overcomes this common challenge by keeping track of its own performance and making changes as necessary to each model without requiring manual intervention. We demonstrate that this novel approach outperforms available alternatives on benchmark datasets in many scenarios.