# Computational pathology in renal disease: a comprehensive perspective

arXiv: https://arxiv.org/abs/2210.10162 · HTML: https://mlsanity.com/paper/2210.10162/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 126 references that are in the map.
- First posted on arXiv 18 Oct 2022.

## Builds on (most cited first, 10 of 10)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Network Dissection: Quantifying Interpretability of Deep Visual Representations](https://mlsanity.com/paper/1704.05796/) (2017)
- [Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks](https://mlsanity.com/paper/1910.01279/) (2019)
- [Methods for Interpreting and Understanding Deep Neural Networks](https://mlsanity.com/paper/1706.07979/) (2017)
- [Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology](https://mlsanity.com/paper/1902.06543/) (2019)
- [Grad-CAM: Why did you say that?](https://mlsanity.com/paper/1611.07450/) (2016)
- [Neural Image Compression for Gigapixel Histopathology Image Analysis](https://mlsanity.com/paper/1811.02840/) (2018)
- [Deep learning-based transformation of the H&E stain into special stains](https://mlsanity.com/paper/2008.08871/) (2020)
- [Development and Evaluation of a Deep Neural Network for Histologic Classification of Renal Cell Carcinoma on Biopsy and Surgical Resection Slides](https://mlsanity.com/paper/2010.16380/) (2020)

## Abstract (arXiv)

Computational pathology is a field that has complemented various subspecialties of diagnostic pathology over the last few years. In this article a brief analyzis the different applications in nephrology is developed. To begin, an overview of the different forms of image production is provided. To continue, the most frequent applications of computer vision models, the salient features of the different clinical applications, and the data protection considerations encountered are described. To finish the development, I delve into the interpretability of these applications, expanding in depth on the three dimensions of this area.