From single to universal: tiny lesion detection in medical imaging

Abstract Accurate and automatic detection of tiny lesions in medical imaging plays a critical role in comprehensive cancer diagnosis, staging, treatment, follow-up, and prognosis. Numerous deep learning (DL) based studies have focused on single lesions, providing highly sensitive identification and precise localization. On the other hand, some recent studies have started to concentrate on whole-body lesions, as they could provide systemic clinical support. This paper presents a single-to-universal review of DL studies on tiny lesion detection in medical imaging, with a particular emphasis on detection models and techniques, as well as the data-related aspects such as modality, dimension, and dataset. A wide range of tasks are covered, including traditional single lesion detection tasks such as lung nodules, breast masses, thyroid nodules, and diseased lymph nodes, as well as the emerging task of universal lesion detection. Moreover, the paper provides in-depth analysis and discussion, with the hope of inspiring future research endeavors.