S3-compatible object storage has become ubiquitous, used by an ever-expanding range of applications. Workload traces show that many of these applications treat object storage like a traditional file system, with many small reads and writes, yet object storage implementations have not kept up. Optimized for bulk storage, these systems cannot efficiently exploit modern SSDs, requiring large hardware installations to achieve operation rates typical of local file systems on modest machines.

We demonstrate that AI models can accurately segment liver tumors without the need for manual annotation by using synthetic tumors in CT scans. Our synthetic tumors have two intriguing advantages: (I) realistic in shape and texture, which even medical professionals can confuse with real tumors; (II) effective for training AI models, which can perform liver tumor segmentation similarly to the model trained on real tumors—this result is exciting because no existing work, using synthetic tumors only, has thus far reached a similar or even close performance to real tumors. This result also implies that manual efforts for annotating tumors voxel by voxel (which took years to create) can be significantly reduced in the future. Moreover, our synthetic tumors can automatically generate many examples of small (or even tiny) synthetic tumors and have the potential to im-prove the success rate of detecting small liver tumors, which is critical for detecting the early stages of cancer. In addition to enriching the training data, our synthesizing strategy also enables us to rigorously assess the AI robustness.