ArticleNature communications2021
An annotation-free whole-slide training approach to pathological classification of lung cancer types using deep learning.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 88 papers, 1 of them a synthesis that pooled it.
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Who cites it
88 citing papers in PubMed, 1 synthesis or guideline pooled it, 217 citations in OpenAlex.
- Machine learning for the detection and diagnosis of cognitive impairment in Parkinson's Disease: A systematic review.PloS one · 2024Pooled it
- HistoEncoder: A digital pathology foundation model for prostate cancer.Journal of pathology informatics · 2026Article
- Integration of Histologic Pattern Classifiers and Graph Convolutional Networks for Prognostic Prediction in Lung Adenocarcinoma.JTO clinical and research reports · 2026Article
- Implementing trust in non-small cell lung cancer diagnosis with a conformalized uncertainty-aware AI framework.Nature biomedical engineering · 2026Article
- A deep learning framework for histopathological analysis of pixel-level extracellular matrix variation in standard H&E-stained images.Scientific reports · 2026Article
- Optimizing surgical decision-making for lung adenocarcinoma by preoperatively identifying pathological high-risk factors: retrospective development and prospective validation of a deep learning model.International journal of surgery (London, England) · 2026Article
- Integrating Artificial Intelligence into Breast Cancer Histopathology: Toward Improved Diagnosis and Prognosis.Cancers · 2026Review
- Label-free pathological subtyping of non-small cell lung cancer using deep classification and virtual immunohistochemical staining.NPJ digital medicine · 2026Article
- Harnessing pyroptosis in breast cancer therapy: immunological mechanisms and emerging biomaterial strategies.Cell death discovery · 2026Review
- Towards deep-learning based detection and quantification of intestinal metaplasia on digitized gastric biopsies: a multi-expert comparative study.Scientific reports · 2026Article
- Multimodal deep learning for survival prediction and biomarker discovery in non-small cell lung cancer.BioData mining · 2026Article
- Assessing the influence of two deep-learning assistance modes on pathologists in cancer identification.Diagnostic pathology · 2026Article
- Deep learning for malignancy and tumor origin prediction using cytology or histopathology whole slide images.NPJ digital medicine · 2026Article
- Exploring phenotype-related single-cells through attention-enhanced representation learning.Genome medicine · 2026Article
- Artificial intelligence in digital pathology diagnosis and analysis: technologies, challenges, and future prospects.Military Medical Research · 2026Review
- Application of deep learning in prognostic prediction of non-small cell lung cancer.Frontiers in cell and developmental biology · 2026Review
- Automated tumor regression grade assessment and survival prediction in esophageal cancer via weakly supervised multiple instance learning.Frontiers in medicine · 2026Article
- Artificial Intelligence in Histopathological Analysis for Predicting Immunotherapy Response in Cutaneous Melanoma.International journal of molecular sciences · 2025Review
- Artificial Intelligence as a Tool in the Diagnosis of Bladder Cancer: A Narrative Review.Cureus · 2025Review
- Recent advances in the study of FAT family genes in lung cancer.Discover oncology · 2025Review
28 more citing papers are in PubMed but not listed here.
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Authors and funding
9 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning for digital pathology is hindered by the extremely high spatial resolution of whole-slide images (WSIs). Most studies have employed patch-based methods, which often require detailed annotation of image patches. This typically involves laborious free-hand contouring on WSIs. To alleviate the burden of such contouring and obtain benefits from scaling up training with numerous WSIs, we develop a method for training neural networks on entire WSIs using only slide-level diagnoses. Our method leverages the unified memory mechanism to overcome the memory constraint of compute accelerators. Experiments conducted on a data set of 9662 lung cancer WSIs reveal that the proposed method achieves areas under the receiver operating characteristic curve of 0.9594 and 0.9414 for adenocarcinoma and squamous cell carcinoma classification on the testing set, respectively. Furthermore, the method demonstrates higher classification performance than multiple-instance learning as well as strong localization results for small lesions through class activation mapping.
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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.