# Understanding the Influence of Receptive Field and Network Complexity in Neural-Network-Guided TEM Image Analysis

arXiv: https://arxiv.org/abs/2204.04250 · HTML: https://mlsanity.com/paper/2204.04250/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 16 references that are in the map.
- First posted on arXiv 8 Apr 2022.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Revisiting Unreasonable Effectiveness of Data in Deep Learning Era](https://mlsanity.com/paper/1707.02968/) (2017)
- [Understanding the Effective Receptive Field in Deep Convolutional Neural Networks](https://mlsanity.com/paper/1701.04128/) (2017)
- [The Receptive Field as a Regularizer in Deep Convolutional Neural Networks for Acoustic Scene Classification](https://mlsanity.com/paper/1907.01803/) (2019)
- [Smoothed Dilated Convolutions for Improved Dense Prediction](https://mlsanity.com/paper/1808.08931/) (2018)
- [Machine Learning Pipeline for Segmentation and Defect Identification from High Resolution Transmission Electron Microscopy Data](https://mlsanity.com/paper/2001.05022/) (2020)
- [Deep Learning Segmentation of Complex Features in Atomic-Resolution Phase Contrast Transmission Electron Microscopy Images](https://mlsanity.com/paper/2012.05322/) (2020)
- [Developing and Evaluating Deep Neural Network-based Denoising for Nanoparticle TEM Images with Ultra-low Signal-to-Noise](https://mlsanity.com/paper/2101.07770/) (2021)

## Built on by (most cited first, 9 of 9)

- [Self-Supervised Learning with Generative Adversarial Networks for Electron Microscopy](https://mlsanity.com/paper/2402.18286/) (2024)
- [A robust synthetic data generation framework for machine learning in High-Resolution Transmission Electron Microscopy (HRTEM)](https://mlsanity.com/paper/2309.06122/) (2023)
- [Demystifying the Effect of Receptive Field Size in U-Net Models for Medical Image Segmentation](https://mlsanity.com/paper/2406.16701/) (2024)
- [Generalization Across Experimental Parameters in Machine Learning Analysis of High Resolution Transmission Electron Microscopy Datasets](https://mlsanity.com/paper/2306.11853/) (2023)
- [Revealing the Evolution of Order in Materials Microstructures Using Multi-Modal Computer Vision](https://mlsanity.com/paper/2411.09896/) (2024)
- [SAM-EM: Real-Time Segmentation for Automated Liquid Phase Transmission Electron Microscopy](https://mlsanity.com/paper/2501.03153/) (2025)
- [DNA Origami Nanostructures Observed in Transmission Electron Microscopy Images can be Characterized through Convolutional Neural Networks](https://mlsanity.com/paper/2503.10950/) (2025)
- [Contrast transfer functions help quantify neural network out-of-distribution generalization in HRTEM](https://mlsanity.com/paper/2512.09067/) (2025)
- [Investigating the Visual Cues of CNNs for Vascular Segmentation: A Case Study in Microscopy and Fundus Imaging](https://mlsanity.com/paper/2607.23371/) (2026)

## Abstract (arXiv)

Trained neural networks are promising tools to analyze the ever-increasing amount of scientific image data, but it is unclear how to best customize these networks for the unique features in transmission electron micrographs. Here, we systematically examine how neural network architecture choices affect how neural networks segment, or pixel-wise separate, crystalline nanoparticles from amorphous background in transmission electron microscopy (TEM) images. We focus on decoupling the influence of receptive field, or the area of the input image that contributes to the output decision, from network complexity, which dictates the number of trainable parameters. We find that for low-resolution TEM images which rely on amplitude contrast to distinguish nanoparticles from background, the receptive field does not significantly influence segmentation performance. On the other hand, for high-resolution TEM images which rely on a combination of amplitude and phase contrast changes to identify nanoparticles, receptive field is a key parameter for increased performance, especially in images with minimal amplitude contrast. Our results provide insight and guidance as to how to adapt neural networks for applications with TEM datasets.