ArticleCancers2023
Annotation-Efficient Deep Learning Model for Pancreatic Cancer Diagnosis and Classification Using CT Images: A Retrospective Diagnostic Study.
Article in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 4 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
16 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Deep learning models for pancreatic cancer detection on CT: a meta-analysis.Frontiers in medicine · 2026Pooled it
- Accuracy of machine learning models for pre-diagnosis and diagnosis of pancreatic ductal adenocarcinoma in contrast-CT images: a systematic review and meta-analysis.Abdominal radiology (New York) · 2025Pooled it
- Diagnosis methods for pancreatic cancer with the technique of deep learning: a review and a meta-analysis.Frontiers in oncology · 2025Pooled it
- From radiomics to transformers in pancreatic cancer detection and prognosis.Frontiers in medicine · 2025Pooled it
- Artificial intelligence in pancreatic cancer: applications in early detection, tumor staging, and survival prediction-a comprehensive review.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- From Bench to Bedside: The Path Toward Real-World Translation for Artificial Intelligence in Pancreatic Cancer Detection.Korean journal of radiology · 2026Review
- Maximizing pancreatic carcinoma classification performance using parrot optimized vision transformer.Scientific reports · 2026Article
- Pancreatic tumor detection in computed tomography images through a rotary positional siamese vision transformer.Scientific reports · 2026Article
- Ensemble deep learning model based on CT scans: differentiating and subtype-classifying pancreatic inflammations and tumors, and predicting pancreatic lesion invasiveness.Quantitative imaging in medicine and surgery · 2026Article
- A Vision Transformer-Based Deep Learning Framework for Patient-Level Classification of Acute Pancreatitis and Normal Pancreas Using Computed Tomography.Diagnostics (Basel, Switzerland) · 2026Article
- Early detection of pancreatic cancer on computed tomography: advancements with deep learning.Radiology advances · 2025Review
- Deep Learning in Digital Breast Tomosynthesis: Current Status, Challenges, and Future Trends.MedComm · 2025Review
- Automated CAD system for early detection and classification of pancreatic cancer using deep learning model.PloS one · 2025Article
- Using the GoogLeNet deep-learning model to distinguish between benign and malignant breast masses based on conventional ultrasound: a systematic review and meta-analysis.Quantitative imaging in medicine and surgery · 2024Article
- From Machine Learning to Patient Outcomes: A Comprehensive Review of AI in Pancreatic Cancer.Diagnostics (Basel, Switzerland) · 2024Review
- Identifying Effective Biomarkers for Accurate Pancreatic Cancer Prognosis Using Statistical Machine Learning.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
The aim of this study was to develop a novel deep learning (DL) model without requiring large-annotated training datasets for detecting pancreatic cancer (PC) using computed tomography (CT) images. This retrospective diagnostic study was conducted using CT images collected from 2004 and 2019 from 4287 patients diagnosed with PC. We proposed a self-supervised learning algorithm (pseudo-lesion segmentation (PS)) for PC classification, which was trained with and without PS and validated on randomly divided training and validation sets. We further performed cross-racial external validation using open-access CT images from 361 patients. For internal validation, the accuracy and sensitivity for PC classification were 94.3% (92.8-95.4%) and 92.5% (90.0-94.4%), and 95.7% (94.5-96.7%) and 99.3 (98.4-99.7%) for the convolutional neural network (CNN) and transformer-based DL models (both with PS), respectively. Implementing PS on a small-sized training dataset (randomly sampled 10%) increased accuracy by 20.5% and sensitivity by 37.0%. For external validation, the accuracy and sensitivity were 82.5% (78.3-86.1%) and 81.7% (77.3-85.4%) and 87.8% (84.0-90.8%) and 86.5% (82.3-89.8%) for the CNN and transformer-based DL models (both with PS), respectively. PS self-supervised learning can increase DL-based PC classification performance, reliability, and robustness of the model for unseen, and even small, datasets. The proposed DL model is potentially useful for PC diagnosis.
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Registered trials
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