In Vivo Computational Strategy for Tumor Targeting in Noisy Scenarios

Because of the inefficiency of conventional medical imaging techniques for early tumor detection, we proposed the framework of in vivo computation in the previous work. Tumor tissue induces the generation of biological gradient field (BGF), and an external magnetic field is utilized to guide nanoswimmers in the search space to find the location of the tumor. Since various kinds of noise are generated in the process of tumor targeting, it is necessary to perform some measures to minimize the impact of noise. Thus, we propose a resampling strategy for in vivo computation and verify its effectiveness through some simulation experiments. The results show that the in vivo computational resampling strategy has significant noise filtering effects in almost all cases.