Cloud computing gives users much freedom on where they host their computation and storage. However the CO 2 emission of a job depends on the location and the energy efficiency of the data centers where it is run. We developed a decision framework that determines to move computation with accompanying data from a local to a greener remote data center for lower CO 2 emissions. The model underlying the framework accounts for the energy consumption at the local and remote sites, as well as of networks among them. We showed that the type of network connecting the two sites has a significant impact on the total CO 2 emission. Furthermore, the task's complexity is a factor in deciding when and where to move computation.
In this paper, we propose a pipeline to investigate the performance of semantic segmentation model that employs an encoder-decoder architecture with atrous separable convolution and spatial pyramid pooling, trained on multi-resolution whole slide breast pathological images with different patch sizes. Our segmentation model obtains the best performance on zoom level 2 (10$$\times $$
×
magnification) with AUC score 0.974 in terms of slide-level classification. This outperforms both the performance of the pathologist and other semantic segmentation models on the Camelyon16 dataset. By offering a larger field of view and reducing noise and detail, training a semantic segmentation model on the properly selected lower resolution pathology images can further improve the precision of pixel-wise cancer region segmentation. By contrast, the corresponding inference time is 14 times shorter than the inference time trained on the highest resolution patches, and it is also shorter than the time required by a pathologist with time constraints. Moreover, we prove that the model trained on lower resolution patches can still generate refined external polygons of cancer region on the highest resolution image. This study provides new insights into efficient gigapixel histopathology analysis that will make clinical adoption more likely.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.