# DLSIA: Deep Learning for Scientific Image Analysis

arXiv: https://arxiv.org/abs/2308.02559 · HTML: https://mlsanity.com/paper/2308.02559/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 91 references that are in the map.
- First posted on arXiv 2 Aug 2023.

## Builds on (most cited first, 25 of 25)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Multi-Scale Context Aggregation by Dilated Convolutions](https://mlsanity.com/paper/1511.07122/) (2015)
- [Striving for Simplicity: The All Convolutional Net](https://mlsanity.com/paper/1412.6806/) (2014)
- [A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification](https://mlsanity.com/paper/2107.07511/) (2021)
- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation](https://mlsanity.com/paper/1912.05074/) (2019)
- [What is the State of Neural Network Pruning?](https://mlsanity.com/paper/2003.03033/) (2020)
- [Image Inpainting for Irregular Holes Using Partial Convolutions](https://mlsanity.com/paper/1804.07723/) (2018)
- [Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data](https://mlsanity.com/paper/2002.12880/) (2020)
- [The Importance of Skip Connections in Biomedical Image Segmentation](https://mlsanity.com/paper/1608.04117/) (2016)
- [Image inpainting: A review](https://mlsanity.com/paper/1909.06399/) (2019)
- [Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future](https://mlsanity.com/paper/2001.07092/) (2020)
- [Domain-adversarial neural networks to address the appearance variability of histopathology images](https://mlsanity.com/paper/1707.06183/) (2017)
- [Faster CNNs with Direct Sparse Convolutions and Guided Pruning](https://mlsanity.com/paper/1608.01409/) (2016)
- [U-SegNet: Fully Convolutional Neural Network based Automated Brain tissue segmentation Tool](https://mlsanity.com/paper/1806.04429/) (2018)
- [MLExchange: A web-based platform enabling exchangeable machine learning workflows for scientific studies](https://mlsanity.com/paper/2208.09751/) (2022)
- [X-ray Scattering Image Classification Using Deep Learning](https://mlsanity.com/paper/1611.03313/) (2016)
- [SHADHO: Massively Scalable Hardware-Aware Distributed Hyperparameter Optimization](https://mlsanity.com/paper/1707.01428/) (2017)
- [LEAN: graph-based pruning for convolutional neural networks by extracting longest chains](https://mlsanity.com/paper/2011.06923/) (2020)
- [Distributing Deep Learning Hyperparameter Tuning for 3D Medical Image Segmentation](https://mlsanity.com/paper/2110.15884/) (2021)

## Built on by (most cited first, 4 of 4)

- [Probabilistic Conformal Coverage Guarantees in Small-Data Settings](https://mlsanity.com/paper/2509.15349/) (2025)
- [qlty: handling large tensors in scientific imaging](https://mlsanity.com/paper/2407.04920/) (2024)
- [Image Segmentation using U-Net Architecture for Powder X-ray Diffraction Images](https://mlsanity.com/paper/2310.16186/) (2023)
- [Machine Learning-Augmented Acceleration of Iterative Ptychographic Reconstruction](https://mlsanity.com/paper/2605.01122/) (2026)

## Abstract (arXiv)

We introduce DLSIA (Deep Learning for Scientific Image Analysis), a Python-based machine learning library that empowers scientists and researchers across diverse scientific domains with a range of customizable convolutional neural network (CNN) architectures for a wide variety of tasks in image analysis to be used in downstream data processing, or for experiment-in-the-loop computing scenarios. DLSIA features easy-to-use architectures such as autoencoders, tunable U-Nets, and parameter-lean mixed-scale dense networks (MSDNets). Additionally, we introduce sparse mixed-scale networks (SMSNets), generated using random graphs and sparse connections. As experimental data continues to grow in scale and complexity, DLSIA provides accessible CNN construction and abstracts CNN complexities, allowing scientists to tailor their machine learning approaches, accelerate discoveries, foster interdisciplinary collaboration, and advance research in scientific image analysis.