ArticleNPJ precision oncology2022
A deep learning model for molecular label transfer that enables cancer cell identification from histopathology images.
Article in NPJ precision oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of Artificial Intelligence in the diagnosis and treatment of colorectal cancer: a bibliometric analysis, 2004-2023.Frontiers in oncology · 2024Pooled it
- Autoencoders for unsupervised analysis of rat myeloarchitecture.Brain structure & function · 2026Article
- Effective transfer of tumor annotations from hematoxylin and eosin to fluorescence images of breast and lung tissues.Journal of biomedical optics · 2026Article
- Engineering the Future of ADCs in Non-Small Cell Lung Cancer.Oncology research · 2026Review
- Cross-Modality Learning for Predicting Immunohistochemistry Biomarkers from Hematoxylin and Eosin-Stained Whole Slide Images.The American journal of pathology · 2025Article
- From traditional to deep learning approaches in whole slide image registration: A methodological review.Journal of pathology informatics · 2025Review
- Development and clinical validation of deep learning-based immunohistochemistry prediction models for subtyping and staging of gastrointestinal cancers.BMC gastroenterology · 2025Article
- Tumour purity assessment with deep learning in colorectal cancer and impact on molecular analysis.The Journal of pathology · 2025Article
- Deep learning-based classification of breast cancer molecular subtypes from H&E whole-slide images.Journal of pathology informatics · 2025Article
- Role of artificial intelligence in cancer detection using protein p53: A Review.Molecular biology reports · 2024Review
- Computational pathology: A survey review and the way forward.Journal of pathology informatics · 2024Review
- Artificial intelligence-based assessment of PD-L1 expression in diffuse large B cell lymphoma.NPJ precision oncology · 2024Article
- Enabling large-scale screening of Barrett's esophagus using weakly supervised deep learning in histopathology.Nature communications · 2024Article
- Optimized models and deep learning methods for drug response prediction in cancer treatments: a review.PeerJ. Computer science · 2024Article
- A Multi-Stain Breast Cancer Histological Whole-Slide-Image Data Set from Routine Diagnostics.Scientific data · 2023Article
- Generalising uncertainty improves accuracy and safety of deep learning analytics applied to oncology.Scientific reports · 2023Article
- Colorectal Cancer Metastases in the Liver Establish Immunosuppressive Spatial Networking between Tumor-Associated SPP1+ Macrophages and Fibroblasts.Clinical cancer research : an official journal of the American Association for Cancer Research · 2023Article
- Recent advances of pathomics in colorectal cancer diagnosis and prognosis.Frontiers in oncology · 2023Review
- Deep Learning-Based Prediction of Molecular Tumor Biomarkers from H&E: A Practical Review.Journal of personalized medicine · 2022Review
- The seen and the unseen: Molecular classification and image based-analysis of gastrointestinal cancers.Computational and structural biotechnology journal · 2022Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Deep-learning classification systems have the potential to improve cancer diagnosis. However, development of these computational approaches so far depends on prior pathological annotations and large training datasets. The manual annotation is low-resolution, time-consuming, highly variable and subject to observer variance. To address this issue, we developed a method, H&E Molecular neural network (HEMnet). HEMnet utilizes immunohistochemistry as an initial molecular label for cancer cells on a H&E image and trains a cancer classifier on the overlapping clinical histopathological images. Using this molecular transfer method, HEMnet successfully generated and labeled 21,939 tumor and 8782 normal tiles from ten whole-slide images for model training. After building the model, HEMnet accurately identified colorectal cancer regions, which achieved 0.84 and 0.73 of ROC AUC values compared to p53 staining and pathological annotations, respectively. Our validation study using histopathology images from TCGA samples accurately estimated tumor purity, which showed a significant correlation (regression coefficient of 0.8) with the estimation based on genomic sequencing data. Thus, HEMnet contributes to addressing two main challenges in cancer deep-learning analysis, namely the need to have a large number of images for training and the dependence on manual labeling by a pathologist. HEMnet also predicts cancer cells at a much higher resolution compared to manual histopathologic evaluation. Overall, our method provides a path towards a fully automated delineation of any type of tumor so long as there is a cancer-oriented molecular stain available for subsequent learning. Software, tutorials and interactive tools are available at: https://github.com/BiomedicalMachineLearning/HEMnet.
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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.