# Generalizable Whole Slide Image Classification with Fine-Grained Visual-Semantic Interaction

arXiv: https://arxiv.org/abs/2402.19326 · HTML: https://mlsanity.com/paper/2402.19326/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 41 references that are in the map.
- First posted on arXiv 29 Feb 2024.

## Builds on (most cited first, 30 of 30)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [Learning to Prompt for Vision-Language Models](https://mlsanity.com/paper/2109.01134/) (2021)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [Conditional Prompt Learning for Vision-Language Models](https://mlsanity.com/paper/2203.05557/) (2022)
- [CoCa: Contrastive Captioners are Image-Text Foundation Models](https://mlsanity.com/paper/2205.01917/) (2022)
- [Attention-based Deep Multiple Instance Learning](https://mlsanity.com/paper/1802.04712/) (2018)
- [LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention](https://mlsanity.com/paper/2303.16199/) (2023)
- [Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images](https://mlsanity.com/paper/2004.09666/) (2020)
- [MaPLe: Multi-modal Prompt Learning](https://mlsanity.com/paper/2210.03117/) (2022)
- [FILIP: Fine-grained Interactive Language-Image Pre-Training](https://mlsanity.com/paper/2111.07783/) (2021)
- [LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model](https://mlsanity.com/paper/2304.15010/) (2023)
- [LiT: Zero-Shot Transfer with Locked-image text Tuning](https://mlsanity.com/paper/2111.07991/) (2021)
- [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 Language-Image Pre-training via Masking](https://mlsanity.com/paper/2212.00794/) (2022)
- [DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classification](https://mlsanity.com/paper/2203.12081/) (2022)
- [Prompt Distribution Learning](https://mlsanity.com/paper/2205.03340/) (2022)
- [Revisiting Multiple Instance Neural Networks](https://mlsanity.com/paper/1610.02501/) (2016)
- [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)
- [Multiple Instance Learning Framework with Masked Hard Instance Mining for Whole Slide Image Classification](https://mlsanity.com/paper/2307.15254/) (2023)
- [Task-specific Fine-tuning via Variational Information Bottleneck for Weakly-supervised Pathology Whole Slide Image Classification](https://mlsanity.com/paper/2303.08446/) (2023)
- [Towards a Visual-Language Foundation Model for Computational Pathology](https://mlsanity.com/paper/2307.12914/) (2023)
- [Histopathology Whole Slide Image Analysis with Heterogeneous Graph Representation Learning](https://mlsanity.com/paper/2307.04189/) (2023)
- [Text-guided Foundation Model Adaptation for Pathological Image Classification](https://mlsanity.com/paper/2307.14901/) (2023)
- [Weakly supervised multiple instance learning histopathological tumor segmentation](https://mlsanity.com/paper/2004.05024/) (2020)
- [The Rise of AI Language Pathologists: Exploring Two-level Prompt Learning for Few-shot Weakly-supervised Whole Slide Image Classification](https://mlsanity.com/paper/2305.17891/) (2023)

## Built on by (most cited first, 27 of 27)

- [SlideChat: A Large Vision-Language Assistant for Whole-Slide Pathology Image Understanding](https://mlsanity.com/paper/2410.11761/) (2024)
- [Interpretable Vision-Language Survival Analysis with Ordinal Inductive Bias for Computational Pathology](https://mlsanity.com/paper/2409.09369/) (2024)
- [Free Lunch in Pathology Foundation Model: Task-specific Model Adaptation with Concept-Guided Feature Enhancement](https://mlsanity.com/paper/2411.09894/) (2024)
- [Queryable Prototype Multiple Instance Learning with Vision-Language Models for Incremental Whole Slide Image Classification](https://mlsanity.com/paper/2410.10573/) (2024)
- [A New Era in Computational Pathology: A Survey on Foundation and Vision-Language Models](https://mlsanity.com/paper/2408.14496/) (2024)
- [SurvMamba: State Space Model with Multi-grained Multi-modal Interaction for Survival Prediction](https://mlsanity.com/paper/2404.08027/) (2024)
- [Visual and Textual Prompts in VLLMs for Enhancing Emotion Recognition](https://mlsanity.com/paper/2504.17224/) (2025)
- [Multi-Resolution Pathology-Language Pre-training Model with Text-Guided Visual Representation](https://mlsanity.com/paper/2504.18856/) (2025)
- [From large language models to multimodal AI: A scoping review on the potential of generative AI in medicine](https://mlsanity.com/paper/2502.09242/) (2025)
- [Diagnostic Text-guided Representation Learning in Hierarchical Classification for Pathological Whole Slide Image](https://mlsanity.com/paper/2411.10709/) (2024)
- [Enhancing Zero-Shot Brain Tumor Subtype Classification via Fine-Grained Patch-Text Alignment](https://mlsanity.com/paper/2508.01602/) (2025)
- [PathFLIP: Fine-grained Language-Image Pretraining for Versatile Computational Pathology](https://mlsanity.com/paper/2512.17621/) (2025)
- [PathVQ: Reforming Computational Pathology Foundation Model for Whole Slide Image Analysis via Vector Quantization](https://mlsanity.com/paper/2503.06482/) (2025)
- [Large-vocabulary forensic pathological analyses via prototypical cross-modal contrastive learning](https://mlsanity.com/paper/2407.14904/) (2024)
- [Efficient Multi-Slide Visual-Language Feature Fusion for Placental Disease Classification](https://mlsanity.com/paper/2508.03277/) (2025)
- [Unlocking adaptive digital pathology through dynamic feature learning](https://mlsanity.com/paper/2412.20430/) (2024)
- [MExD: An Expert-Infused Diffusion Model for Whole-Slide Image Classification](https://mlsanity.com/paper/2503.12401/) (2025)
- [Exploiting Low-Dimensional Manifold of Features for Few-Shot Whole Slide Image Classification](https://mlsanity.com/paper/2505.15504/) (2025)
- [Multiple Instance Learning Framework with Masked Hard Instance Mining for Gigapixel Histopathology Image Analysis](https://mlsanity.com/paper/2509.11526/) (2025)
- [VLM-based Prompts as the Optimal Assistant for Unpaired Histopathology Virtual Staining](https://mlsanity.com/paper/2504.15545/) (2025)
- [MOC: Meta-Optimized Classifier for Few-Shot Whole Slide Image Classification](https://mlsanity.com/paper/2508.09967/) (2025)
- [Revisiting Data Challenges of Computational Pathology: A Pack-based Multiple Instance Learning Training Framework](https://mlsanity.com/paper/2509.20923/) (2025)
- [Multimodal Feature Prototype Learning for Interpretable and Discriminative Cancer Survival Prediction](https://mlsanity.com/paper/2510.06113/) (2025)
- [HyperST: Hierarchical Hyperbolic Learning for Spatial Transcriptomics Prediction](https://mlsanity.com/paper/2511.22107/) (2025)
- [WSD-MIL: Window Scale Decay Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2512.19982/) (2025)
- [PA-MIL: Phenotype-Aware Multiple Instance Learning Guided by Language Prompting and Genotype-to-Phenotype Relationships](https://mlsanity.com/paper/2602.02558/) (2026)
- [AINet: Anchor Instances Learning for Regional Heterogeneity in Whole Slide Image](https://mlsanity.com/paper/2602.20187/) (2026)

## Abstract (arXiv)

Whole Slide Image (WSI) classification is often formulated as a Multiple Instance Learning (MIL) problem. Recently, Vision-Language Models (VLMs) have demonstrated remarkable performance in WSI classification. However, existing methods leverage coarse-grained pathogenetic descriptions for visual representation supervision, which are insufficient to capture the complex visual appearance of pathogenetic images, hindering the generalizability of models on diverse downstream tasks. Additionally, processing high-resolution WSIs can be computationally expensive. In this paper, we propose a novel "Fine-grained Visual-Semantic Interaction" (FiVE) framework for WSI classification. It is designed to enhance the model's generalizability by leveraging the interaction between localized visual patterns and fine-grained pathological semantics. Specifically, with meticulously designed queries, we start by utilizing a large language model to extract fine-grained pathological descriptions from various non-standardized raw reports. The output descriptions are then reconstructed into fine-grained labels used for training. By introducing a Task-specific Fine-grained Semantics (TFS) module, we enable prompts to capture crucial visual information in WSIs, which enhances representation learning and augments generalization capabilities significantly. Furthermore, given that pathological visual patterns are redundantly distributed across tissue slices, we sample a subset of visual instances during training. Our method demonstrates robust generalizability and strong transferability, dominantly outperforming the counterparts on the TCGA Lung Cancer dataset with at least 9.19% higher accuracy in few-shot experiments. The code is available at: https://github.com/ls1rius/WSI_FiVE.