Before that, it is crucial to develop benchmarks to evaluate L VLMs' effectiveness in various medical applications.

Surveillance systems [HC11] are equipped with a suite of sensors that can be switched and steered to focus attention on any target or location over a physical landscape (see Figure 1) to detect abrupt changes at any location. On the other hand, sensor suites are resource limited, and only a limited subset, among all the locations, can be probed at any time.

However, the automation of MIS and RAMIS through the use of AI has been slow due to difficulties in data acquisition and curation, partially caused by the ethical considerations of training, testing and deploying AI models in medical environments.

In addition, we examine two common computation frameworks for this problem, i.e., population-based training (PBT) and iterative learning

Retinal vessel segmentation is generally grounded in image-based datasets collected with bench-top devices.