Lung cancer is the deadliest form of cancer mainly because of the absence of reliable early diagnostic protocols. Therefore, there is increasing interest in the development of novel diagnostic noninvasive technologies that may improve the early detection of the disease. Bronchoscope-guided bronchoalveolar lavage (BAL) is a minimally invasive diagnostic technique that is based on the extraction and analysis of cellular material from the bronchial epithelium of patients that present suspicious lung masses on low-dose screening X-ray-computed tomography images. Together with a novel staining technique that combines immunophenotyping of a lung cancer biomarker with fluorescent in situ hybridization of genetically abnormal DNA loci, BAL promises a powerful early diagnostic tool for lung carcinomas. The sensitivity of this method, however, is highly dependent on the pathologist's ability to reliably and repeatedly examine thousands of cells under the microscope. This is an extremely labor-intensive and error-prone task. We have developed a multiscale multidimensional integrated microscopy computer-aided detection platform that autonomously scans and analyzes BAL samples. In this paper, we describe its software architecture and validate the specific image analysis protocols that are developed for this particular application.