# Monte-Carlo Sampling applied to Multiple Instance Learning for Histological Image Classification

arXiv: https://arxiv.org/abs/1812.11560 · HTML: https://mlsanity.com/paper/1812.11560/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Dec 2018.

## Built on by (most cited first, 7 of 7)

- [Accounting for Temporal Variability in Functional Magnetic Resonance Imaging Improves Prediction of Intelligence](https://mlsanity.com/paper/2211.07429/) (2022)
- [Hard Exudate Segmentation Supplemented by Super-Resolution with Multi-scale Attention Fusion Module](https://mlsanity.com/paper/2211.09404/) (2022)
- [Video-SwinUNet: Spatio-temporal Deep Learning Framework for VFSS Instance Segmentation](https://mlsanity.com/paper/2302.11325/) (2023)
- [Selecting the Best Optimizers for Deep Learning based Medical Image Segmentation](https://mlsanity.com/paper/2302.02289/) (2023)
- [DCELANM-Net:Medical Image Segmentation based on Dual Channel Efficient Layer Aggregation Network with Learner](https://mlsanity.com/paper/2304.09620/) (2023)
- [Revealing the Underlying Patterns: Investigating Dataset Similarity, Performance, and Generalization](https://mlsanity.com/paper/2308.03580/) (2023)
- [Spatial-temporal V-Net for automatic segmentation and quantification of right ventricles in gated myocardial perfusion SPECT images](https://mlsanity.com/paper/2110.05443/) (2021)

## Abstract (arXiv)

We propose a patch sampling strategy based on a sequential Monte-Carlo method for high resolution image classification in the context of Multiple Instance Learning. When compared with grid sampling and uniform sampling techniques, it achieves higher generalization performance. We validate the strategy on two artificial datasets and two histological datasets for breast cancer and sun exposure classification.