ArticleNPJ digital medicine2024
Aligning knowledge concepts to whole slide images for precise histopathology image analysis.
Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning prediction of MSI/dMMR, EBV, HER2, PD-L1, and CLDN18.2 status directly from hematoxylin and eosin-stained slides in gastric and gastroesophageal junction adenocarcinoma: a systematic review.Frontiers in oncology · 2026Pooled it
- Review
- Diffusion attention expert model for predicting and semi-automatic localizing STAS in lung cancer histopathological images.Nature communications · 2026Article
- CNet-Cox for interpretable network biomarker discovery and survival risk scoring in precise breast cancer prognosis.NPJ digital medicine · 2026Article
- Current trends and future directions of artificial intelligence in lung cancer diagnosis.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2026Article
- Malignant vs. Non-malignant Annotations on TCGA Breast Cancer Whole Slide Images for AI Analysis.Scientific data · 2026Article
- Generative artificial intelligence in lung cancer care: current applications, challenges, and future directions.Frontiers in oncology · 2026Review
- Prior knowledge-guided multimodal deep learning system for biomarker exploration and prognosis prediction of urothelial carcinoma.NPJ digital medicine · 2025Article
- Foundation models: Insights and implications for gastrointestinal cancer.World journal of gastroenterology · 2025Review
- Multimodal deep learning model for prediction of breast cancer recurrence risk and correlation with oncotype DX.Breast cancer research : BCR · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 disease-specific 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 the 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 tasks, ConcepPath significantly outperformed previous SOTA methods, which lacked the guidance of human expert knowledge.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.