# Boostlet.js: Image processing plugins for the web via JavaScript injection

arXiv: https://arxiv.org/abs/2405.07868 · HTML: https://mlsanity.com/paper/2405.07868/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 17 references that are in the map.
- First posted on arXiv 13 May 2024.

## Builds on (most cited first, 3 of 3)

- [The NCI Imaging Data Commons as a platform for reproducible research in computational pathology](https://mlsanity.com/paper/2303.09354/) (2023)
- [TRAKO: Efficient Transmission of Tractography Data for Visualization](https://mlsanity.com/paper/2004.13630/) (2020)
- [Brainchop: Next Generation Web-Based Neuroimaging Application](https://mlsanity.com/paper/2310.16162/) (2023)

## Abstract (arXiv)

Can web-based image processing and visualization tools easily integrate into existing websites without significant time and effort? Our Boostlet.js library addresses this challenge by providing an open-source, JavaScript-based web framework to enable additional image processing functionalities. Boostlet examples include kernel filtering, image captioning, data visualization, segmentation, and web-optimized machine-learning models. To achieve this, Boostlet.js uses a browser bookmark to inject a user-friendly plugin selection tool called PowerBoost into any host website. Boostlet also provides on-site access to a standard API independent of any visualization framework for pixel data and scene manipulation. Web-based Boostlets provide a modular architecture and client-side processing capabilities to apply advanced image-processing techniques using consumer-level hardware. The code is open-source and available.