# A Multi-resolution Model for Histopathology Image Classification and Localization with Multiple Instance Learning

arXiv: https://arxiv.org/abs/2011.02679 · HTML: https://mlsanity.com/paper/2011.02679/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Nov 2020.

## Built on by (most cited first, 30 of 35)

- [Additive MIL: Intrinsically Interpretable Multiple Instance Learning for Pathology](https://mlsanity.com/paper/2206.01794/) (2022)
- [Differentiable Zooming for Multiple Instance Learning on Whole-Slide Images](https://mlsanity.com/paper/2204.12454/) (2022)
- [Diagnose Like a Pathologist: Transformer-Enabled Hierarchical Attention-Guided Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2301.08125/) (2023)
- [AMIGO: Sparse Multi-Modal Graph Transformer with Shared-Context Processing for Representation Learning of Giga-pixel Images](https://mlsanity.com/paper/2303.00865/) (2023)
- [Efficient Classification of Very Large Images with Tiny Objects](https://mlsanity.com/paper/2106.02694/) (2021)
- [CAMIL: Context-Aware Multiple Instance Learning for Cancer Detection and Subtyping in Whole Slide Images](https://mlsanity.com/paper/2305.05314/) (2023)
- [Shapley Values-enabled Progressive Pseudo Bag Augmentation for Whole Slide Image Classification](https://mlsanity.com/paper/2312.05490/) (2023)
- [A Hierarchical Conditional Random Field-based Attention Mechanism Approach for Gastric Histopathology Image Classification](https://mlsanity.com/paper/2102.10499/) (2021)
- [Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology](https://mlsanity.com/paper/2501.02922/) (2025)
- [Nested Multiple Instance Learning with Attention Mechanisms](https://mlsanity.com/paper/2111.00947/) (2021)
- [PDBL: Improving Histopathological Tissue Classification with Plug-and-Play Pyramidal Deep-Broad Learning](https://mlsanity.com/paper/2111.03063/) (2021)
- [Advances in Multiple Instance Learning for Whole Slide Image Analysis: Techniques, Challenges, and Future Directions](https://mlsanity.com/paper/2408.09476/) (2024)
- [PATHS: A Hierarchical Transformer for Efficient Whole Slide Image Analysis](https://mlsanity.com/paper/2411.18225/) (2024)
- [Interpretable histopathology-based prediction of disease relevant features in Inflammatory Bowel Disease biopsies using weakly-supervised deep learning](https://mlsanity.com/paper/2303.12095/) (2023)
- [Multi-Resolution Pathology-Language Pre-training Model with Text-Guided Visual Representation](https://mlsanity.com/paper/2504.18856/) (2025)
- [Attention2Minority: A salient instance inference-based multiple instance learning for classifying small lesions in whole slide images](https://mlsanity.com/paper/2301.07700/) (2023)
- [CLASS-M: Adaptive stain separation-based contrastive learning with pseudo-labeling for histopathological image classification](https://mlsanity.com/paper/2312.06978/) (2023)
- [A robust and lightweight deep attention multiple instance learning algorithm for predicting genetic alterations](https://mlsanity.com/paper/2206.00455/) (2022)
- [Context-Aware Self-Supervised Learning of Whole Slide Images](https://mlsanity.com/paper/2306.04763/) (2023)
- [Towards Hierarchical Regional Transformer-based Multiple Instance Learning](https://mlsanity.com/paper/2308.12634/) (2023)
- [A review of machine learning approaches, challenges and prospects for computational tumor pathology](https://mlsanity.com/paper/2206.01728/) (2022)
- [CIMIL-CRC: a clinically-informed multiple instance learning framework for patient-level colorectal cancer molecular subtypes classification from H&E stained images](https://mlsanity.com/paper/2401.16131/) (2024)
- [SPADE: Spatial Transcriptomics and Pathology Alignment Using a Mixture of Data Experts for an Expressive Latent Space](https://mlsanity.com/paper/2506.21857/) (2025)
- [TRUSWorthy: Toward Clinically Applicable Deep Learning for Confident Detection of Prostate Cancer in Micro-Ultrasound](https://mlsanity.com/paper/2502.14707/) (2025)
- [Prototype-Guided Diffusion for Digital Pathology: Achieving Foundation Model Performance with Minimal Clinical Data](https://mlsanity.com/paper/2504.12351/) (2025)
- [Sequential Attention-based Sampling for Histopathological Analysis](https://mlsanity.com/paper/2507.05077/) (2025)
- [Integrating multiscale topology in digital pathology with pyramidal graph convolutional networks](https://mlsanity.com/paper/2403.15068/) (2024)
- [MAPLE: Multi-scale Attribute-enhanced Prompt Learning for Few-shot Whole Slide Image Classification](https://mlsanity.com/paper/2509.25863/) (2025)
- [Reliable Benchmarking of Artifact Detection in Computational Pathology: A Reproducibility and Uncertainty Analysis](https://mlsanity.com/paper/2608.30835/) (2026)
- [Adversary-Robust Graph-Based Learning of WSIs](https://mlsanity.com/paper/2403.14489/) (2024)

## Abstract (arXiv)

Histopathological images provide rich information for disease diagnosis. Large numbers of histopathological images have been digitized into high resolution whole slide images, opening opportunities in developing computational image analysis tools to reduce pathologists' workload and potentially improve inter- and intra- observer agreement. Most previous work on whole slide image analysis has focused on classification or segmentation of small pre-selected regions-of-interest, which requires fine-grained annotation and is non-trivial to extend for large-scale whole slide analysis. In this paper, we proposed a multi-resolution multiple instance learning model that leverages saliency maps to detect suspicious regions for fine-grained grade prediction. Instead of relying on expensive region- or pixel-level annotations, our model can be trained end-to-end with only slide-level labels. The model is developed on a large-scale prostate biopsy dataset containing 20,229 slides from 830 patients. The model achieved 92.7% accuracy, 81.8% Cohen's Kappa for benign, low grade (i.e. Grade group 1) and high grade (i.e. Grade group >= 2) prediction, an area under the receiver operating characteristic curve (AUROC) of 98.2% and an average precision (AP) of 97.4% for differentiating malignant and benign slides. The model obtained an AUROC of 99.4% and an AP of 99.8% for cancer detection on an external dataset.