# Feature Re-Embedding: Towards Foundation Model-Level Performance in Computational Pathology

arXiv: https://arxiv.org/abs/2402.17228 · HTML: https://mlsanity.com/paper/2402.17228/

## Key facts

- Cited by 67 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 53 references that are in the map.
- First posted on arXiv 27 Feb 2024.

## Builds on (most cited first, 28 of 28)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Self-Attention with Relative Position Representations](https://mlsanity.com/paper/1803.02155/) (2018)
- [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)
- [Nystr"omformer: A Nystr"om-Based Algorithm for Approximating Self-Attention](https://mlsanity.com/paper/2102.03902/) (2021)
- [A Transformer-based Framework for Multivariate Time Series Representation Learning](https://mlsanity.com/paper/2010.02803/) (2020)
- [Conditional Positional Encodings for Vision Transformers](https://mlsanity.com/paper/2102.10882/) (2021)
- [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)
- [Rethinking and Improving Relative Position Encoding for Vision Transformer](https://mlsanity.com/paper/2107.14222/) (2021)
- [Structure-Aware Transformer for Graph Representation Learning](https://mlsanity.com/paper/2202.03036/) (2022)
- [Whole Slide Images based Cancer Survival Prediction using Attention Guided Deep Multiple Instance Learning Networks](https://mlsanity.com/paper/2009.11169/) (2020)
- [GraphFormers: GNN-nested Transformers for Representation Learning on Textual Graph](https://mlsanity.com/paper/2105.02605/) (2021)
- [Artificial Intelligence for Digital and Computational Pathology](https://mlsanity.com/paper/2401.06148/) (2023)
- [Interventional Bag Multi-Instance Learning On Whole-Slide Pathological Images](https://mlsanity.com/paper/2303.06873/) (2023)
- [CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentation](https://mlsanity.com/paper/1908.10555/) (2019)
- [Cross-Modal Translation and Alignment for Survival Analysis](https://mlsanity.com/paper/2309.12855/) (2023)
- [Cluster-to-Conquer: A Framework for End-to-End Multi-Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2103.10626/) (2021)
- [Multiple Instance Learning Framework with Masked Hard Instance Mining for Whole Slide Image Classification](https://mlsanity.com/paper/2307.15254/) (2023)
- [Deep learning-based survival prediction for multiple cancer types using histopathology images](https://mlsanity.com/paper/1912.07354/) (2019)
- [Self supervised learning improves dMMR/MSI detection from histology slides across multiple cancers](https://mlsanity.com/paper/2109.05819/) (2021)

## Built on by (most cited first, 30 of 67)

- [Do Multiple Instance Learning Models Transfer?](https://mlsanity.com/paper/2506.09022/) (2025)
- [Graph Domain Adaptation with Dual-branch Encoder and Two-level Alignment for Whole Slide Image-based Survival Prediction](https://mlsanity.com/paper/2411.14001/) (2024)
- [MSCPT: Few-shot Whole Slide Image Classification with Multi-scale and Context-focused Prompt Tuning](https://mlsanity.com/paper/2408.11505/) (2024)
- [Agent Aggregator with Mask Denoise Mechanism for Histopathology Whole Slide Image Analysis](https://mlsanity.com/paper/2409.11664/) (2024)
- [Free Lunch in Pathology Foundation Model: Task-specific Model Adaptation with Concept-Guided Feature Enhancement](https://mlsanity.com/paper/2411.09894/) (2024)
- [A New Era in Computational Pathology: A Survey on Foundation and Vision-Language Models](https://mlsanity.com/paper/2408.14496/) (2024)
- [FAST: A Dual-tier Few-Shot Learning Paradigm for Whole Slide Image Classification](https://mlsanity.com/paper/2409.19720/) (2024)
- [SurvMamba: State Space Model with Multi-grained Multi-modal Interaction for Survival Prediction](https://mlsanity.com/paper/2404.08027/) (2024)
- [From Correlation to Causation: Max-Pooling-Based Multi-Instance Learning Leads to More Robust Whole Slide Image Classification](https://mlsanity.com/paper/2408.09449/) (2024)
- [MergeUp-augmented Semi-Weakly Supervised Learning for WSI Classification](https://mlsanity.com/paper/2408.12825/) (2024)
- [MiCo: Multiple Instance Learning with Context-Aware Clustering for Whole Slide Image Analysis](https://mlsanity.com/paper/2506.18028/) (2025)
- [Multi-Resolution Pathology-Language Pre-training Model with Text-Guided Visual Representation](https://mlsanity.com/paper/2504.18856/) (2025)
- [TICON: A Slide-Level Tile Contextualizer for Histopathology Representation Learning](https://mlsanity.com/paper/2512.21331/) (2025)
- [CARE: A Molecular-Guided Foundation Model with Adaptive Region Modeling for Whole Slide Image Analysis](https://mlsanity.com/paper/2602.21637/) (2026)
- [ASMIL: Attention-Stabilized Multiple Instance Learning for Whole Slide Imaging](https://mlsanity.com/paper/2603.06658/) (2026)
- [Multimodal Model for Computational Pathology:Representation Learning and Image Compression](https://mlsanity.com/paper/2603.18660/) (2026)
- [Fusion of Multi-scale Heterogeneous Pathology Foundation Models for Whole Slide Image Analysis](https://mlsanity.com/paper/2510.27237/) (2025)
- [RankByGene: Gene-Guided Histopathology Representation Learning Through Cross-Modal Ranking Consistency](https://mlsanity.com/paper/2411.15076/) (2024)
- [Mixture of Mini Experts: Overcoming the Linear Layer Bottleneck in Multiple Instance Learning](https://mlsanity.com/paper/2603.22198/) (2026)
- [MGPATH: Vision-Language Model with Multi-Granular Prompt Learning for Few-Shot WSI Classification](https://mlsanity.com/paper/2502.07409/) (2025)
- [Can We Simplify Slide-level Fine-tuning of Pathology Foundation Models?](https://mlsanity.com/paper/2502.20823/) (2025)
- [PathVQ: Reforming Computational Pathology Foundation Model for Whole Slide Image Analysis via Vector Quantization](https://mlsanity.com/paper/2503.06482/) (2025)
- [PS3: A Multimodal Transformer Integrating Pathology Reports with Histology Images and Biological Pathways for Cancer Survival Prediction](https://mlsanity.com/paper/2509.20022/) (2025)
- [AtlasPatch: Scalable Foundation Model-based Tissue Detection and Patch Extraction for Computational Pathology](https://mlsanity.com/paper/2602.03998/) (2026)
- [Continual Multiple Instance Learning with Enhanced Localization for Histopathological Whole Slide Image Analysis](https://mlsanity.com/paper/2507.02395/) (2025)
- [HiLa: Hierarchical Vision-Language Collaboration for Cancer Survival Prediction](https://mlsanity.com/paper/2507.04613/) (2025)
- [Multi-modal Knowledge Decomposition based Online Distillation for Biomarker Prediction in Breast Cancer Histopathology](https://mlsanity.com/paper/2508.17213/) (2025)
- [Beyond Attention Heatmaps: How to Get Better Explanations for Multiple Instance Learning Models in Histopathology](https://mlsanity.com/paper/2603.08328/) (2026)
- [Bridging the Modality Bottleneck in Pathology MIL through Virtual Molecular Staining](https://mlsanity.com/paper/2605.16392/) (2026)
- [Thinking in Scales: Accelerating Gigapixel Pathology Image Analysis via Adaptive Continuous Reasoning](https://mlsanity.com/paper/2605.19491/) (2026)

## Abstract (arXiv)

Multiple instance learning (MIL) is the most widely used framework in computational pathology, encompassing sub-typing, diagnosis, prognosis, and more. However, the existing MIL paradigm typically requires an offline instance feature extractor, such as a pre-trained ResNet or a foundation model. This approach lacks the capability for feature fine-tuning within the specific downstream tasks, limiting its adaptability and performance. To address this issue, we propose a Re-embedded Regional Transformer (R2T) for re-embedding the instance features online, which captures fine-grained local features and establishes connections across different regions. Unlike existing works that focus on pre-training powerful feature extractor or designing sophisticated instance aggregator, R2T is tailored to re-embed instance features online. It serves as a portable module that can seamlessly integrate into mainstream MIL models. Extensive experimental results on common computational pathology tasks validate that: 1) feature re-embedding improves the performance of MIL models based on ResNet-50 features to the level of foundation model features, and further enhances the performance of foundation model features; 2) the R2T can introduce more significant performance improvements to various MIL models; 3) R2T-MIL, as an R2T-enhanced AB-MIL, outperforms other latest methods by a large margin.The code is available at: https://github.com/DearCaat/RRT-MIL.