EfficientBioAI: Making Bioimaging AI Models Efficient in Energy, Latency and Representation

Artificial intelligence (AI) has been widely used in bioimage image analysis nowadays, but the efficiency of AI models, like the energy consumption and latency is not ignorable due to the growing model size and complexity, as well as the fast-growing analysis needs in modern biomedical studies. In this work, we present EfficientBioAI, a plug-and-play toolbox that can compress given bioimaging AI models for them to run with significantly reduced energy cost and inference time on both CPU and GPU, without compromise on accuracy. In some cases, the prediction accuracy could even increase after compression, since the compression procedure could remove redundant information in the model representation and therefore reduce over-fitting. From four different bioimage analysis applications, we observed around 2-5 times speed-up during inference and 30-80% saving in energy. Cutting the runtime of large scale bioimage analysis from days to hours or getting a two-minutes bioimaging AI model inference done in near real-time will open new doors for method development and biomedical discoveries. Over the last decade, microscopy bioimaging techniques have been advancing at unprecedented pace, with higher spatial resolution [1], larger imaging volumes [2] and higher throughput for screening [3]. These advancements have also led to the rapid development of artificial intelligence (AI) methods in microscopy image analysis tools (e.g. As the AI-based microscopy image analysis methods setting new records in various benchmarks and permitting quantitative biological studies not feasible before, we want to raise the awareness of another aspect of bioimaging AI models' performance: efficiency.