# ProtoDiv: Prototype-guided Division of Consistent Pseudo-bags for Whole-slide Image Classification

arXiv: https://arxiv.org/abs/2304.06652 · HTML: https://mlsanity.com/paper/2304.06652/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 43 references that are in the map.
- First posted on arXiv 13 Apr 2023.

## Builds on (most cited first, 13 of 13)

- [Attention-based Deep Multiple Instance Learning](https://mlsanity.com/paper/1802.04712/) (2018)
- [Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images](https://mlsanity.com/paper/2004.09666/) (2020)
- [TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2106.00908/) (2021)
- [Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learning](https://mlsanity.com/paper/2011.08939/) (2020)
- [Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning](https://mlsanity.com/paper/2206.02647/) (2022)
- [DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classification](https://mlsanity.com/paper/2203.12081/) (2022)
- [Multiple Instance Learning: A Survey of Problem Characteristics and Applications](https://mlsanity.com/paper/1612.03365/) (2016)
- [Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classification](https://mlsanity.com/paper/1504.07947/) (2015)
- [Revisiting Multiple Instance Neural Networks](https://mlsanity.com/paper/1610.02501/) (2016)
- [AdvMIL: Adversarial Multiple Instance Learning for the Survival Analysis on Whole-Slide Images](https://mlsanity.com/paper/2212.06515/) (2022)
- [DSCA: A Dual-Stream Network with Cross-Attention on Whole-Slide Image Pyramids for Cancer Prognosis](https://mlsanity.com/paper/2206.05782/) (2022)
- [Weakly Supervised Joint Whole-Slide Segmentation and Classification in Prostate Cancer](https://mlsanity.com/paper/2301.02933/) (2023)
- [Evaluating histopathology transfer learning with ChampKit](https://mlsanity.com/paper/2206.06862/) (2022)

## Built on by (most cited first, 5 of 5)

- [Pseudo-Bag Mixup Augmentation for Multiple Instance Learning-Based Whole Slide Image Classification](https://mlsanity.com/paper/2306.16180/) (2023)
- [Shapley Values-enabled Progressive Pseudo Bag Augmentation for Whole Slide Image Classification](https://mlsanity.com/paper/2312.05490/) (2023)
- [Dynamic Policy-Driven Adaptive Multi-Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2403.07939/) (2024)
- [Multiple Instance Learning Framework with Masked Hard Instance Mining for Gigapixel Histopathology Image Analysis](https://mlsanity.com/paper/2509.11526/) (2025)
- [AINet: Anchor Instances Learning for Regional Heterogeneity in Whole Slide Image](https://mlsanity.com/paper/2602.20187/) (2026)

## Abstract (arXiv)

Due to the limitations of inadequate Whole-Slide Image (WSI) samples with weak labels, pseudo-bag-based multiple instance learning (MIL) appears as a vibrant prospect in WSI classification. However, the pseudo-bag dividing scheme, often crucial for classification performance, is still an open topic worth exploring. Therefore, this paper proposes a novel scheme, ProtoDiv, using a bag prototype to guide the division of WSI pseudo-bags. Rather than designing complex network architecture, this scheme takes a plugin-and-play approach to safely augment WSI data for effective training while preserving sample consistency. Furthermore, we specially devise an attention-based prototype that could be optimized dynamically in training to adapt to a classification task. We apply our ProtoDiv scheme on seven baseline models, and then carry out a group of comparison experiments on two public WSI datasets. Experiments confirm our ProtoDiv could usually bring obvious performance improvements to WSI classification.