mlsanityData to 14 Oct 2026

Aligning Knowledge Concepts to Whole Slide Images for Precise Histopathology Image Analysis

arXiv 2411.18101 ↗First posted 27 Nov 2024cs.CV cs.LGcited by 10

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Abstract (arXiv)

Due to the large size and lack of fine-grained annotation, Whole Slide Images (WSIs) analysis is commonly approached as a Multiple Instance Learning (MIL) problem. However, previous studies only learn from training data, posing a stark contrast to how human clinicians teach each other and reason about histopathologic entities and factors. Here we present a novel knowledge concept-based MIL framework, named ConcepPath to fill this gap. Specifically, ConcepPath utilizes GPT-4 to induce reliable diseasespecific human expert concepts from medical literature, and incorporate them with a group of purely learnable concepts to extract complementary knowledge from training data. In ConcepPath, WSIs are aligned to these linguistic knowledge concepts by utilizing pathology vision-language model as the basic building component. In the application of lung cancer subtyping, breast cancer HER2 scoring, and gastric cancer immunotherapy-sensitive subtyping task, ConcepPath significantly outperformed previous SOTA methods which lack the guidance of human expert knowledge.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 24 of its 53 references that are in the map. First posted on arXiv 27 Nov 2024.

Builds on

24 of 53 references.

References · 24

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  3. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  4. 2021Learning to Prompt for Vision-Language Modelscited by 2,281
  5. 2018Attention-based Deep Multiple Instance Learningcited by 1,095
  6. 2021CLIP-Adapter: Better Vision-Language Models with Feature Adapterscited by 1,026
  7. 2020Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Imagescited by 745
  8. 2021TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classificationcited by 555
  9. 2020Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learningcited by 478
  10. 2022Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learningcited by 359
  11. 2022Language in a Bottle: Language Model Guided Concept Bottlenecks for Interpretable Image Classificationcited by 312
  12. 2023Quilt-1M: One Million Image-Text Pairs for Histopathologycited by 194
12 more
  1. 2020Whole Slide Images based Cancer Survival Prediction using Attention Guided Deep Multiple Instance Learning Networkscited by 178
  2. 2015Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classificationcited by 159
  3. 2023Visual Language Pretrained Multiple Instance Zero-Shot Transfer for Histopathology Imagescited by 134
  4. 2019CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentationcited by 73
  5. 2023Learning Concise and Descriptive Attributes for Visual Recognitioncited by 73
  6. 2021Multiple Instance Captioning: Learning Representations from Histopathology Textbooks and Articlescited by 58
  7. 2021Fine-Tuning and Training of DenseNet for Histopathology Image Representation Using TCGA Diagnostic Slidescited by 52
  8. 2023Towards a Visual-Language Foundation Model for Computational Pathologycited by 52
  9. 2023Histopathology Whole Slide Image Analysis with Heterogeneous Graph Representation Learningcited by 47
  10. 2023The Rise of AI Language Pathologists: Exploring Two-level Prompt Learning for Few-shot Weakly-supervised Whole Slide Image Classificationcited by 41
  11. 2021SlideGraph+: Whole Slide Image Level Graphs to Predict HER2Status in Breast Cancercited by 31
  12. 2024Benchmarking PathCLIP for Pathology Image Analysiscited by 6

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 5; 2026: 5.

10 papers.

  1. 2025GECKO: Gigapixel Vision-Concept Contrastive Pretraining in Histopathologycited by 8
  2. 2025MoMA: A Mixture-of-Multimodal-Agents Architecture for Enhancing Clinical Prediction Modellingcited by 7
  3. 2025Knowledge-Driven Vision-Language Model for Plexus Detection in Hirschsprung's Diseasecited by 2
  4. 2026PathMem: Toward Cognition-Aligned Memory Transformation for Pathology MLLMscited by 1
  5. 2025Extreme Learning Machines for Attention-based Multiple Instance Learning in Whole-Slide Image Classificationcited by 0
  6. 2025PathReasoning: A multimodal reasoning agent for query-based ROI navigation on whole-slide imagescited by 0
  7. 2026Diffusion Attention Expert Model for Predicting and Semi-automatic Localizing STAS in Lung Cancer Histopathological Imagescited by 0
  8. 2026SAGE: Semantic Explainability of Attention-Based Survival Models in Computational Pathologycited by 0
  9. 2026Benchmarking Vision-Language Models for Automated Pathology Diagnosis and Report Generationcited by 0
  10. 2026Modeling Whole-Slide Images as Dynamic Tumor Microenvironment Fieldscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology
  2. 2024PathAlign: A vision-language model for whole slide images in histopathology
  3. 2025Slide-Level Prompt Learning with Vision Language Models for Few-Shot Multiple Instance Learning in Histopathology
  4. 2025ChatEXAONEPath: An Expert-level Multimodal Large Language Model for Histopathology Using Whole Slide Images
  5. 2025Prototype-Based Multiple Instance Learning for Gigapixel Whole Slide Image Classification
  6. 2021Self-supervised driven consistency training for annotation efficient histopathology image analysis
  7. 2026Beyond Attention Heatmaps: How to Get Better Explanations for Multiple Instance Learning Models in Histopathology
  8. 2025Histopathology Image Report Generation by Vision Language Model with Multimodal In-Context Learning