# Efficient Whole Slide Image Classification through Fisher Vector Representation

arXiv: https://arxiv.org/abs/2411.08530 · HTML: https://mlsanity.com/paper/2411.08530/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 28 references that are in the map.
- First posted on arXiv 13 Nov 2024.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images](https://mlsanity.com/paper/2004.09666/) (2020)
- [Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classification](https://mlsanity.com/paper/1504.07947/) (2015)
- [Convolutional Networks with Dense Connectivity](https://mlsanity.com/paper/2001.02394/) (2020)
- [Attention-Challenging Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2311.07125/) (2023)
- [Deep Learning for Whole Slide Image Analysis: An Overview](https://mlsanity.com/paper/1910.11097/) (2019)
- [Multiple Instance Learning for Heterogeneous Images: Training a CNN for Histopathology](https://mlsanity.com/paper/1806.05083/) (2018)
- [Pathology-knowledge Enhanced Multi-instance Prompt Learning for Few-shot Whole Slide Image Classification](https://mlsanity.com/paper/2407.10814/) (2024)
- [Efficient Quality Control of Whole Slide Pathology Images with Human-in-the-loop Training](https://mlsanity.com/paper/2409.19587/) (2024)
- [EGFR Mutation Prediction of Lung Biopsy Images using Deep Learning](https://mlsanity.com/paper/2208.12506/) (2022)
- [Prompt-Guided Foundation Model Tuning for Pathology Image Classification](https://mlsanity.com/paper/2403.12537/) (2024)
- [DA-RefineNet:A Dual Input Whole Slide Image Segmentation Algorithm Based on Attention](https://mlsanity.com/paper/1907.06358/) (2019)
- [Understanding the Fisher Vector: a multimodal part model](https://mlsanity.com/paper/1504.04763/) (2015)

## Abstract (arXiv)

The advancement of digital pathology, particularly through computational analysis of whole slide images (WSI), is poised to significantly enhance diagnostic precision and efficiency. However, the large size and complexity of WSIs make it difficult to analyze and classify them using computers. This study introduces a novel method for WSI classification by automating the identification and examination of the most informative patches, thus eliminating the need to process the entire slide. Our method involves two-stages: firstly, it extracts only a few patches from the WSIs based on their pathological significance; and secondly, it employs Fisher vectors (FVs) for representing features extracted from these patches, which is known for its robustness in capturing fine-grained details. This approach not only accentuates key pathological features within the WSI representation but also significantly reduces computational overhead, thus making the process more efficient and scalable. We have rigorously evaluated the proposed method across multiple datasets to benchmark its performance against comprehensive WSI analysis and contemporary weakly-supervised learning methodologies. The empirical results indicate that our focused analysis of select patches, combined with Fisher vector representation, not only aligns with, but at times surpasses, the classification accuracy of standard practices. Moreover, this strategy notably diminishes computational load and resource expenditure, thereby establishing an efficient and precise framework for WSI analysis in the realm of digital pathology.