# qlty: handling large tensors in scientific imaging

arXiv: https://arxiv.org/abs/2407.04920 · HTML: https://mlsanity.com/paper/2407.04920/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 21 references that are in the map.
- First posted on arXiv 6 Jul 2024.

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

- [Cell Detection with Star-convex Polygons](https://mlsanity.com/paper/1806.03535/) (2018)
- [Inferring low-dimensional microstructure representations using convolutional neural networks](https://mlsanity.com/paper/1611.02764/) (2016)
- [DLSIA: Deep Learning for Scientific Image Analysis](https://mlsanity.com/paper/2308.02559/) (2023)

## Built on by (most cited first, 1 of 1)

- [Probabilistic Conformal Coverage Guarantees in Small-Data Settings](https://mlsanity.com/paper/2509.15349/) (2025)

## Abstract (arXiv)

In scientific imaging, deep learning has become a pivotal tool for image analytics. However, handling large volumetric datasets, which often exceed the memory capacity of standard GPUs, require special attention when subjected to deep learning efforts. This paper introduces qlty, a toolkit designed to address these challenges through tensor management techniques. qlty offers robust methods for subsampling, cleaning, and stitching of large-scale spatial data, enabling effective training and inference even in resource-limited environments.