ArticleDiagnostics (Basel, Switzerland)2023
Automatic Classification of Histopathology Images across Multiple Cancers Based on Heterogeneous Transfer Learning.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 16 citations in OpenAlex.
- Enhanced breast cancer detection in histopathology images using graph transformer GANs with graph masked modeling and osprey optimization.Journal of molecular histology · 2026Article
- A histopathology aware DINO model with attention based representation enhancement.Scientific reports · 2025Article
- Deep learning model for grading carcinoma with Gini-based feature selection and linear production-inspired feature fusion.Scientific reports · 2025Article
- Transfer Learning Fusion Approaches for Colorectal Cancer Histopathological Image Analysis.Journal of imaging · 2025Article
- Detecting early gastrointestinal polyps in histology and endoscopy images using deep learning.Frontiers in artificial intelligence · 2025Article
- Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review.Journal of pathology informatics · 2024Article
- Colon and lung cancer classification from multi-modal images using resilient and efficient neural network architectures.Heliyon · 2024Article
- Histopathology image classification: highlighting the gap between manual analysis and AI automation.Frontiers in oncology · 2023Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCurrent artificial intelligence (AI) in histopathology typically specializes on a single task, resulting in a heavy workload of collecting and labeling a sufficient number of images for each type of cancer. Heterogeneous transfer learning (HTL) is expected to alleviate the data bottlenecks and establish models with performance comparable to supervised learning (SL).
methodsAn accurate source domain model was trained using 28,634 colorectal patches. Additionally, 1000 sentinel lymph node patches and 1008 breast patches were used to train two target domain models. The feature distribution difference between sentinel lymph node metastasis or breast cancer and CRC was reduced by heterogeneous domain adaptation, and the maximum mean difference between subdomains was used for knowledge transfer to achieve accurate classification across multiple cancers.
resultHTL on 1000 sentinel lymph node patches (L-HTL-1000) outperforms SL on 1000 sentinel lymph node patches (L-SL-1-1000) (average area under the curve (AUC) and standard deviation of L-HTL-1000 vs. L-SL-1-1000: 0.949 ± 0.004 vs. 0.931 ± 0.008,
conclusionsHTL is capable of building accurate AI models for similar cancers using a small amount of data based on a large dataset for a certain type of cancer. HTL holds great promise for accelerating the development of AI in histopathology.
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