# SOS: Selective Objective Switch for Rapid Immunofluorescence Whole Slide Image Classification

arXiv: https://arxiv.org/abs/2003.05080 · HTML: https://mlsanity.com/paper/2003.05080/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Mar 2020.

## Built on by (most cited first, 12 of 12)

- [A Review of Uncertainty Estimation and its Application in Medical Imaging](https://mlsanity.com/paper/2302.08119/) (2023)
- [Accounting for Dependencies in Deep Learning Based Multiple Instance Learning for Whole Slide Imaging](https://mlsanity.com/paper/2111.01556/) (2021)
- [Gigapixel Whole-Slide Images Classification using Locally Supervised Learning](https://mlsanity.com/paper/2207.08267/) (2022)
- [Cross-scale Multi-instance Learning for Pathological Image Diagnosis](https://mlsanity.com/paper/2304.00216/) (2023)
- [Learning Binary and Sparse Permutation-Invariant Representations for Fast and Memory Efficient Whole Slide Image Search](https://mlsanity.com/paper/2208.13653/) (2022)
- [DSNet: A Dual-Stream Framework for Weakly-Supervised Gigapixel Pathology Image Analysis](https://mlsanity.com/paper/2109.05788/) (2021)
- [Trusted Multi-Scale Classification Framework for Whole Slide Image](https://mlsanity.com/paper/2207.05290/) (2022)
- [Cross-scale Attention Guided Multi-instance Learning for Crohn's Disease Diagnosis with Pathological Images](https://mlsanity.com/paper/2208.07322/) (2022)
- [Magnifying Networks for Images with Billions of Pixels](https://mlsanity.com/paper/2112.06121/) (2021)
- [MDM: Multiple Dynamic Masks for Visual Explanation of Neural Networks](https://mlsanity.com/paper/2207.08046/) (2022)
- [Multiplex-detection Based Multiple Instance Learning Network for Whole Slide Image Classification](https://mlsanity.com/paper/2208.03526/) (2022)
- [Struggle with Adversarial Defense? Try Diffusion](https://mlsanity.com/paper/2404.08273/) (2024)

## Abstract (arXiv)

The difficulty of processing gigapixel whole slide images (WSIs) in clinical microscopy has been a long-standing barrier to implementing computer aided diagnostic systems. Since modern computing resources are unable to perform computations at this extremely large scale, current state of the art methods utilize patch-based processing to preserve the resolution of WSIs. However, these methods are often resource intensive and make significant compromises on processing time. In this paper, we demonstrate that conventional patch-based processing is redundant for certain WSI classification tasks where high resolution is only required in a minority of cases. This reflects what is observed in clinical practice; where a pathologist may screen slides using a low power objective and only switch to a high power in cases where they are uncertain about their findings. To eliminate these redundancies, we propose a method for the selective use of high resolution processing based on the confidence of predictions on downscaled WSIs --- we call this the Selective Objective Switch (SOS). Our method is validated on a novel dataset of 684 Liver-Kidney-Stomach immunofluorescence WSIs routinely used in the investigation of autoimmune liver disease. By limiting high resolution processing to cases which cannot be classified confidently at low resolution, we maintain the accuracy of patch-level analysis whilst reducing the inference time by a factor of 7.74.