Computational pathology in renal disease: a comprehensive perspective
Not on the map. Why?
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.
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
10 of 126 references.
References · 10
- 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,733
- 2015Learning Deep Features for Discriminative Localizationcited by 2,436
- 2017Network Dissection: Quantifying Interpretability of Deep Visual Representationscited by 708
- 2019Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networkscited by 582
- 2017Methods for Interpreting and Understanding Deep Neural Networkscited by 345
- 2019Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathologycited by 173
- 2016Grad-CAM: Why did you say that?cited by 161
- 2018Neural Image Compression for Gigapixel Histopathology Image Analysiscited by 65
- 2020Deep learning-based transformation of the H&E stain into special stainscited by 55
- 2020Development and Evaluation of a Deep Neural Network for Histologic Classification of Renal Cell Carcinoma on Biopsy and Surgical Resection Slidescited by 24
Built on by
Not cited yet.