Spatial CUSUM for Signal Region Detection
This addresses the challenge of efficient and precise signal detection for applications like medical imaging and epidemiology, though it appears incremental as it builds on existing CUSUM and FDR concepts.
The paper tackles the problem of detecting weak clustered signals in spatial data, such as in medical imaging and epidemiology, by proposing a novel Spatial CUSUM (SCUSUM) method that achieves high classification accuracy asymptotically and demonstrates sensitivity to weak signals in simulations, detecting more irregular signals in real fMRI data compared to existing methods.
Detecting weak clustered signal in spatial data is important but challenging in applications such as medical image and epidemiology. A more efficient detection algorithm can provide more precise early warning, and effectively reduce the decision risk and cost. To date, many methods have been developed to detect signals with spatial structures. However, most of the existing methods are either too conservative for weak signals or computationally too intensive. In this paper, we consider a novel method named Spatial CUSUM (SCUSUM), which employs the idea of the CUSUM procedure and false discovery rate controlling. We develop theoretical properties of the method which indicates that asymptotically SCUSUM can reach high classification accuracy. In the simulation study, we demonstrate that SCUSUM is sensitive to weak spatial signals. This new method is applied to a real fMRI dataset as illustration, and more irregular weak spatial signals are detected in the images compared to some existing methods, including the conventional FDR, FDR$_L$ and scan statistics.