As psychophysical studies are resource-intensive to conduct, model observers are commonly used to assess and optimize medical imaging quality. Existing model observers were typically designed to detect at most one signal. However, in clinical practice, there may be multiple abnormalities in a single image set (e.g., multifocal and multicentric breast cancers (MMBC)), which can impact treatment planning. Prevalence of signals can be different across anatomical regions, and human observers do not know the number or location of signals a priori. As new imaging techniques have the potential to improve multiple-signal detection (e.g., digital breast tomosynthesis may be more effective for diagnosis of MMBC than planar mammography), image quality assessment approaches addressing such tasks are needed. In this study, we present a model-observer mechanism to detect multiple signals in the same image dataset. To handle the high dimensionality of images, a novel implementation of partial least squares (PLS) was developed to estimate different sets of efficient channels directly from the images. Without any prior knowledge of the background or the signals, the PLS channels capture interactions between signals and the background which provide discriminant image information. Corresponding linear decision templates are employed to generate both image-level and location-specific scores on the presence of signals. Our preliminary results show that the model observer using PLS channels, compared to our first attempts with Laguerre-Gauss channels, can achieve high performance with a reasonably small number of channels, and the optimal design of the model observer may vary as the tasks of clinical interest change.