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

arXiv: https://arxiv.org/abs/2306.06152 · HTML: https://mlsanity.com/paper/2306.06152/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 27 references that are in the map.
- First posted on arXiv 9 Jun 2023.

## Builds on (most cited first, 11 of 11)

- [Learning both Weights and Connections for Efficient Neural Networks](https://mlsanity.com/paper/1506.02626/) (2015)
- [Knowledge Distillation: A Survey](https://mlsanity.com/paper/2006.05525/) (2020)
- [MONAI: An open-source framework for deep learning in healthcare](https://mlsanity.com/paper/2211.02701/) (2022)
- [Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration](https://mlsanity.com/paper/1811.00250/) (2018)
- [MQBench: Towards Reproducible and Deployable Model Quantization Benchmark](https://mlsanity.com/paper/2111.03759/) (2021)
- [Neural Network Quantization with AI Model Efficiency Toolkit (AIMET)](https://mlsanity.com/paper/2201.08442/) (2022)
- [Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation](https://mlsanity.com/paper/1803.04907/) (2018)
- [Neural Network Compression Framework for fast model inference](https://mlsanity.com/paper/2002.08679/) (2020)
- [REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs](https://mlsanity.com/paper/1909.13396/) (2019)
- [U-Net Fixed-Point Quantization for Medical Image Segmentation](https://mlsanity.com/paper/1908.01073/) (2019)
- [MMV_Im2Im: An Open Source Microscopy Machine Vision Toolbox for Image-to-Image Transformation](https://mlsanity.com/paper/2209.02498/) (2022)

## Built on by (most cited first, 2 of 2)

- [Defining the boundaries: challenges and advances in identifying cells in microscopy images](https://mlsanity.com/paper/2311.08269/) (2023)
- [SustainDiffusion: Optimising the Social and Environmental Sustainability of Stable Diffusion Models](https://mlsanity.com/paper/2507.15663/) (2025)

## Abstract (arXiv)

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. Like we can compress large images for efficient storage and sharing, we can also compress the AI models for efficient applications and deployment. 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. We hope our toolbox will facilitate resource-constrained bioimaging AI and accelerate large-scale AI-based quantitative biological studies in an eco-friendly way, as well as stimulate further research on the efficiency of bioimaging AI.