mlsanityData to 14 Oct 2026

DLSIA: Deep Learning for Scientific Image Analysis

arXiv 2308.02559 ↗First posted 2 Aug 2023cs.CV cs.LG hep-excited by 4

Not on the map. Why?

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.

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

25 of 91 references.

References · 25

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 7,005
  4. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,527
  5. 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,898
  6. 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,525
  7. 2015Multi-Scale Context Aggregation by Dilated Convolutionscited by 1,133
  8. 2014Striving for Simplicity: The All Convolutional Netcited by 990
  9. 2021A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantificationcited by 792
  10. 2016EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfacescited by 628
  11. 2019UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentationcited by 586
  12. 2020What is the State of Neural Network Pruning?cited by 583
13 more
  1. 2018Image Inpainting for Irregular Holes Using Partial Convolutionscited by 505
  2. 2020Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Datacited by 248
  3. 2016The Importance of Skip Connections in Biomedical Image Segmentationcited by 126
  4. 2019Image inpainting: A reviewcited by 89
  5. 2020Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Futurecited by 83
  6. 2017Domain-adversarial neural networks to address the appearance variability of histopathology imagescited by 41
  7. 2016Faster CNNs with Direct Sparse Convolutions and Guided Pruningcited by 39
  8. 2018U-SegNet: Fully Convolutional Neural Network based Automated Brain tissue segmentation Toolcited by 11
  9. 2022MLExchange: A web-based platform enabling exchangeable machine learning workflows for scientific studiescited by 4
  10. 2016X-ray Scattering Image Classification Using Deep Learningcited by 3
  11. 2017SHADHO: Massively Scalable Hardware-Aware Distributed Hyperparameter Optimizationcited by 2
  12. 2020LEAN: graph-based pruning for convolutional neural networks by extracting longest chainscited by 2
  13. 2021Distributing Deep Learning Hyperparameter Tuning for 3D Medical Image Segmentationcited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 1; 2026: 1.

4 papers.

  1. 2025Probabilistic Conformal Coverage Guarantees in Small-Data Settingscited by 2
  2. 2024qlty: handling large tensors in scientific imagingcited by 1
  3. 2023Image Segmentation using U-Net Architecture for Powder X-ray Diffraction Imagescited by 0
  4. 2026Machine Learning-Augmented Acceleration of Iterative Ptychographic Reconstructioncited by 0