# From augmented microscopy to the topological transformer: a new approach in cell image analysis for Alzheimer's research

arXiv: https://arxiv.org/abs/2108.01625 · HTML: https://mlsanity.com/paper/2108.01625/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 67 references that are in the map.
- First posted on arXiv 3 Aug 2021.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [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)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [An introduction to Topological Data Analysis: fundamental and practical aspects for data scientists](https://mlsanity.com/paper/1710.04019/) (2017)
- [A Primer on the Signature Method in Machine Learning](https://mlsanity.com/paper/1603.03788/) (2016)
- [Signatory: differentiable computations of the signature and logsignature transforms, on both CPU and GPU](https://mlsanity.com/paper/2001.00706/) (2020)
- [Stochastic Convergence of Persistence Landscapes and Silhouettes](https://mlsanity.com/paper/1312.0308/) (2013)
- [Topological Approaches to Deep Learning](https://mlsanity.com/paper/1811.01122/) (2018)
- [Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation](https://mlsanity.com/paper/1609.01006/) (2016)
- [Persistence paths and signature features in topological data analysis](https://mlsanity.com/paper/1806.00381/) (2018)
- [Using persistent homology and dynamical distances to analyze protein binding](https://mlsanity.com/paper/1412.1394/) (2014)
- [Topological Data Analysis of Task-Based fMRI Data from Experiments on Schizophrenia](https://mlsanity.com/paper/1809.08504/) (2018)

## Abstract (arXiv)

Cell image analysis is crucial in Alzheimer's research to detect the presence of Aβ protein inhibiting cell function. Deep learning speeds up the process by making only low-level data sufficient for fruitful inspection. We first found Unet is most suitable in augmented microscopy by comparing performance in multi-class semantics segmentation. We develop the augmented microscopy method to capture nuclei in a brightfield image and the transformer using Unet model to convert an input image into a sequence of topological information. The performance regarding Intersection-over-Union is consistent concerning the choice of image preprocessing and ground-truth generation. Training model with data of a specific cell type demonstrates transfer learning applies to some extent. The topological transformer aims to extract persistence silhouettes or landscape signatures containing geometric information of a given image of cells. This feature extraction facilitates studying an image as a collection of one-dimensional data, substantially reducing computational costs. Using the transformer, we attempt grouping cell images by their cell type relying solely on topological features. Performances of the transformers followed by SVM, XGBoost, LGBM, and simple convolutional neural network classifiers are inferior to the conventional image classification. However, since this research initiates a new perspective in biomedical research by combining deep learning and topology for image analysis, we speculate follow-up investigation will reinforce our genuine regime.