ArticleMedical physics2022
Lung cancer diagnosis using deep attention-based multiple instance learning and radiomics.
Article in Medical physics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 39 citations in OpenAlex.
- LungDxFormer: a transformer-CNN hybrid model with dynamic spatial attention for accurate lung cancer detection and classification.Scientific reports · 2026Article
- CT-based multiple instance and ensemble learning for lymph node metastasis prediction in esophageal squamous cell carcinoma: a multicentre, retrospective study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Research on liver cancer pathology image recognition based on deep learning image processing.Scientific reports · 2026Article
- GLAPAL-H: Global, Local, and Parts Aware Learner for Hydrocephalus Infection Diagnosis in Low-Field MRI.IEEE transactions on bio-medical engineering · 2026Article
- Development and validation of a multimodal radiomics-serum biomarker model for diagnosing solid pulmonary nodules via machine learning.Journal of thoracic disease · 2025Article
- Deep Learning-Driven Multimodal Integration of miRNA and Radiomic for Lung Cancer Diagnosis.Biosensors · 2025Review
- Integrative habitat analysis and multi-instance deep learning for predictive model of PD-1/PD-L1 immunotherapy efficacy in NSCLC patients: a dual-center retrospective study.BMC medical imaging · 2025Article
- Utilization of artificial intelligence-based pulmonary nodule target reconstruction software in clinical practice education for standardized training residents.BMC medical education · 2025Article
- 2.5D deep learning radiomics and clinical data for predicting occult lymph node metastasis in lung adenocarcinoma.BMC medical imaging · 2025Article
- GLAPAL-H: Global, Local, And Parts Aware Learner for Hydrocephalus Infection Diagnosis in Low-Field MRI.medRxiv : the preprint server for health sciences · 2025Article
- AI-Powered Clinical Decision Support Systems in Disease Diagnosis, Treatment Planning, and Prognosis: A Systematic Review.Medical journal of the Islamic Republic of Iran · 2025Review
- RadShap: An Explanation Tool for Highlighting the Contributions of Multiple Regions of Interest to the Prediction of Radiomic Models.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2024Article
- Patient-level thyroid cancer classification using attention multiple instance learning on fused multi-scale ultrasound image features.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- Development and verification of radiomics framework for computed tomography image segmentation.Medical physics · 2022Article
- Improving reproducibility and performance of radiomics in low-dose CT using cycle GANs.Journal of applied clinical medical physics · 2022Article
- Lung cancer diagnosis using deep attention-based multiple instance learning and radiomics.Medical physics · 2022Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundEarly diagnosis of lung cancer is a key intervention for the treatment of lung cancer in which computer-aided diagnosis (CAD) can play a crucial role. Most published CAD methods perform lung cancer diagnosis by classifying each lung nodule in isolation. However, this does not reflect clinical practice, where clinicians diagnose a patient based on a set of images of nodules, instead of looking at one nodule at a time. Besides, the low interpretability of the output provided by these methods presents an important barrier for their adoption.
methodIn this article, we treat lung cancer diagnosis as a multiple instance learning (MIL) problem, which better reflects the diagnosis process in the clinical setting and provides higher interpretability of the output. We selected radiomics as the source of input features and deep attention-based MIL as the classification algorithm. The attention mechanism provides higher interpretability by estimating the importance of each instance in the set for the final diagnosis. To improve the model's performance in a small imbalanced dataset, we propose a new bag simulation method for MIL. RESULTS AND
conclusionThe results show that our method can achieve a mean accuracy of 0.807 with a standard error of the mean (SEM) of 0.069, a recall of 0.870 (SEM 0.061), a positive predictive value of 0.928 (SEM 0.078), a negative predictive value of
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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.